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Data Science in Engineering Process - Product Lifecycle Management
Siemens Advanta Consulting

How to develop digital products and solutions for industrial environments?

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The Data Science and Engineering Process in PLM.

Huge opportunities for digital products are accompanied by huge risks

Digitalization is about to profoundly change the way we live and work. The increasing availability of data combined with growing storage capacities and computing power make it possible to create data-based products, services, and customer specific solutions to create insight with value for the business. Successful implementation requires systematic procedures for managing and analyzing data, but today such procedures are not covered in the PLM processes.

From our experience in industrial settings, organizations start processing the data that happens to be available. This data often does not fully cover the situation of interest, typically has poor quality, and in turn the results of data analysis are misleading. In industrial environments, the reliability and accuracy of results are crucial. Therefore, an enormous responsibility comes with the development of digital products and solutions. Unless there are systematic procedures in place to guide data management and data analysis in the development lifecycle, many promising digital products will not meet expectations.

Various methodologies exist but no comprehensive framework

Over the last decades, various methodologies focusing on specific aspects of how to deal with data were promoted across industries and academia. Examples are Six Sigma, CRISP-DM, JDM standard, DMM model, and KDD process. These methodologies aim at introducing principles for systematic data management and data analysis. Each methodology makes an important contribution to the overall picture of how to deal with data, but none provides a comprehensive framework covering all the necessary tasks and activities for the development of digital products. We should take these approaches as valuable input and integrate their strengths into a comprehensive Data Science and Engineering framework.

In fact, we believe it is time to establish an independent discipline to address the specific challenges of developing digital products, services and customer specific solutions. We need the same kind of professionalism in dealing with data that has been achieved in the established branches of engineering.

Data Science and Engineering as new discipline

Whereas the implementation of software algorithms is adequately guided by software engineering practices, there is currently no established engineering discipline covering the important tasks that focus on the data and how to develop causal models that capture the real world. We believe the development of industrial grade digital products and services requires an additional process area comprising best practices for data management and data analysis. This process area addresses the specific roles, skills, tasks, methods, tools, and management that are needed to succeed.

Figure: Data Science and Engineering as new engineering discipline

More than in other engineering disciplines, the outputs of Data Science and Engineering are created in repetitions of tasks in iterative cycles. The tasks are therefore organized into workflows with distinct objectives that clearly overlap along the phases of the PLM process.

Feasibility of Objectives
  Understand the business situation, confirm the feasibility of the product idea, clarify the data infrastructure needs, and create transparency on opportunities and risks related to the product idea from the data perspective.
Domain Understanding
  Establish an understanding of the causal context of the application domain, identify the influencing factors with impact on the outcomes in the operational scenarios where the digital product or service is going to be used.
Data Management
  Develop the data management strategy, define policies on data lifecycle management, design the specific solution architecture, and validate the technical solution after implementation.
Data Collection
  Define, implement and execute operational procedures for selecting, pre-processing, and transforming data as basis for further analysis. Ensure data quality by performing measurement system analysis and data integrity checks.
Modeling
  Select suitable modeling techniques and create a calibrated prediction model, which includes fitting the parameters or training the model and verifying the accuracy and precision of the prediction model.
Insight Provision
  Incorporate the prediction model into a digital product or solution, provide suitable visualizations to address the information needs, evaluate the accuracy of the prediction results, and establish feedback loops.

Real business value will be generated only if the prediction model at the core of the digital product reliably and accurately reflects the real world, and the results allow to derive not only correct but also helpful conclusions. Now is the time to embrace the unique chances by establishing professionalism in data science and engineering.

Authors

Peter Louis                               

Peter Louis is working at Siemens Advanta Consulting as Senior Key Expert. He has 25 years’ experience in Project Management, Quality Management, Software Engineering, Statistical Process Control, and various process frameworks (Lean, Agile, CMMI). He is an expert on SPC, KPI systems, data analytics, prediction modelling, and Six Sigma Black Belt.

Ralf Russ    

Ralf Russ works as a Principal Key Expert at Siemens Advanta Consulting. He has more than two decades experience rolling out frameworks for development of industrial-grade high quality products, services, and solutions. He is Six Sigma Master Black Belt and passionate about process transparency, optimization, anomaly detection, and prediction modelling using statistics and data analytics.4

Benjamin Aunkofer

Six properties of modern Business Intelligence

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Regardless of the industry in which you operate, you need information systems that evaluate your business data in order to provide you with a basis for decision-making. These systems are commonly referred to as so-called business intelligence (BI). In fact, most BI systems suffer from deficiencies that can be eliminated. In addition, modern BI can partially automate decisions and enable comprehensive analyzes with a high degree of flexibility in use.

Read this article in German:
“Sechs Eigenschaften einer modernen Business Intelligence“

Let us discuss the six characteristics that distinguish modern business intelligence, which mean taking technical tricks into account in detail, but always in the context of a great vision for your own company BI:

1. Uniform database of high quality

Every managing director certainly knows the situation that his managers do not agree on how many costs and revenues actually arise in detail and what the margins per category look like. And if they do, this information is often only available months too late.

Every company has to make hundreds or even thousands of decisions at the operational level every day, which can be made much more well-founded if there is good information and thus increase sales and save costs. However, there are many source systems from the company’s internal IT system landscape as well as other external data sources. The gathering and consolidation of information often takes up entire groups of employees and offers plenty of room for human error.

A system that provides at least the most relevant data for business management at the right time and in good quality in a trusted data zone as a single source of truth (SPOT). SPOT is the core of modern business intelligence.

In addition, other data on BI may also be made available which can be useful for qualified analysts and data scientists. For all decision-makers, the particularly trustworthy zone is the one through which all decision-makers across the company can synchronize.

2. Flexible use by different stakeholders

Even if all employees across the company should be able to access central, trustworthy data, with a clever architecture this does not exclude that each department receives its own views of this data. Many BI systems fail due to company-wide inacceptance because certain departments or technically defined employee groups are largely excluded from BI.

Modern BI systems enable views and the necessary data integration for all stakeholders in the company who rely on information and benefit equally from the SPOT approach.

3. Efficient ways to expand (time to market)

The core users of a BI system are particularly dissatisfied when the expansion or partial redesign of the information system requires too much of patience. Historically grown, incorrectly designed and not particularly adaptable BI systems often employ a whole team of IT staff and tickets with requests for change requests.

Good BI is a service for stakeholders with a short time to market. The correct design, selection of software and the implementation of data flows / models ensures significantly shorter development and implementation times for improvements and new features.

Furthermore, it is not only the technology that is decisive, but also the choice of organizational form, including the design of roles and responsibilities – from the technical system connection to data preparation, pre-analysis and support for the end users.

4. Integrated skills for Data Science and AI

Business intelligence and data science are often viewed and managed separately from each other. Firstly, because data scientists are often unmotivated to work with – from their point of view – boring data models and prepared data. On the other hand, because BI is usually already established as a traditional system in the company, despite the many problems that BI still has today.

Data science, often referred to as advanced analytics, deals with deep immersion in data using exploratory statistics and methods of data mining (unsupervised machine learning) as well as predictive analytics (supervised machine learning). Deep learning is a sub-area of ​​machine learning and is used for data mining or predictive analytics. Machine learning is a sub-area of ​​artificial intelligence (AI).

In the future, BI and data science or AI will continue to grow together, because at the latest after going live, the prediction models flow back into business intelligence. BI will probably develop into ABI (Artificial Business Intelligence). However, many companies are already using data mining and predictive analytics in the company, using uniform or different platforms with or without BI integration.

Modern BI systems also offer data scientists a platform to access high-quality and more granular raw data.

5. Sufficiently high performance

Most readers of these six points will probably have had experience with slow BI before. It takes several minutes to load a daily report to be used in many classic BI systems. If loading a dashboard can be combined with a little coffee break, it may still be acceptable for certain reports from time to time. At the latest, however, with frequent use, long loading times and unreliable reports are no longer acceptable.

One reason for poor performance is the hardware, which can be almost linearly scaled to higher data volumes and more analysis complexity using cloud systems. The use of cloud also enables the modular separation of storage and computing power from data and applications and is therefore generally recommended, but not necessarily the right choice for all companies.

In fact, performance is not only dependent on the hardware, the right choice of software and the right choice of design for data models and data flows also play a crucial role. Because while hardware can be changed or upgraded relatively easily, changing the architecture is associated with much more effort and BI competence. Unsuitable data models or data flows will certainly bring the latest hardware to its knees in its maximum configuration.

6. Cost-effective use and conclusion

Professional cloud systems that can be used for BI systems offer total cost calculators, such as Microsoft Azure, Amazon Web Services and Google Cloud. With these computers – with instruction from an experienced BI expert – not only can costs for the use of hardware be estimated, but ideas for cost optimization can also be calculated. Nevertheless, the cloud is still not the right solution for every company and classic calculations for on-premise solutions are necessary.

Incidentally, cost efficiency can also be increased with a good selection of the right software. Because proprietary solutions are tied to different license models and can only be compared using application scenarios. Apart from that, there are also good open source solutions that can be used largely free of charge and can be used for many applications without compromises.

However, it is wrong to assess the cost of a BI only according to its hardware and software costs. A significant part of cost efficiency is complementary to the aspects for the performance of the BI system, because suboptimal architectures work wastefully and require more expensive hardware than neatly coordinated architectures. The production of the central data supply in adequate quality can save many unnecessary processes of data preparation and many flexible analysis options also make redundant systems unnecessary and lead to indirect savings.

In any case, a BI for companies with many operational processes is always cheaper than no BI. However, if you take a closer look with BI expertise, cost efficiency is often possible.

Signavio

Seeing the Big Picture: Combining Enterprise Architecture with Process Management

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Ever tried watching a 3D movie without those cool glasses people like to take home? Two hours of blurred flashing images is no-one’s idea of fun. But with the right equipment, you get an immersive experience, with realistic, clear, and focused images popping out of the screen. In the same way, the right enterprise architecture brings the complex structure of an organization into focus.

We know that IT environments in today’s modern businesses consist of a growing number of highly complex, interconnected, and often difficult-to-manage IT systems. Balancing customer service and efficiency imperatives associated with social, mobile, cloud, and big data technologies, along with effective day-to-day IT functions and support, can often feel like an insurmountable challenge.

Enterprise architecture can help organizations achieve this balance, all while managing risk, optimizing costs, and implementing innovations. Its main aim is to support reform and transformation programs. To do this, enterprise architecture relies on the accuracy of an enterprise’s complex data systems, and takes into account changing standards, regulations, and strategic business demands.

Components of effective enterprise architecture

In general, most widely accepted enterprise architecture frameworks consist of four interdependent domains:

  • Business Architecture

A blueprint of the enterprise that provides a common understanding of the organization, and used to align strategic objectives and tactical demands. An example would be representing business processes using business process management notation.

  • Data Architecture

The domain that shows the dependencies and connections between an organization’s data, rules, models, and standards.

  • Applications Architecture

The layer that shows a company’s complete set of software solutions and their relationships with each other.

  • Infrastructure Architecture

Positioned at the lowest level, this component shows the relationships and connections of an organization’s existing hardware solutions.

Effective EA implementation means employees within a business can build a clear understanding of the way their company’s IT systems execute their specific work processes, as well as how they interact and relate to each other. It allows users to identify and analyze application and business performance, with the goal of enabling underperforming IT systems to be promptly and efficiently managed.

In short, EA helps businesses answer questions like:

  • Which IT systems are in use, and where, and by whom?
  • Which business processes relate to which IT systems?
  • Who is responsible for which IT systems?
  • How well are privacy protection requirements upheld?
  • Which server is each application run on?

The same questions, shifted slightly to refer to business processes rather than IT systems, are what drive enterprise-level business process management as well. Is it any wonder the two disciplines go together like popcorn and a good movie?

Combining enterprise architecture with process management

Successful business/IT alignment involves effectively leveraging an organization’s IT to achieve company goals and requirements. Standardized language and images (like flow charts and graphs) are often helpful in fostering mutual understanding between highly technical IT services and the business side of an organization.

In the same way, combining EA with collaborative business process management establishes a common language throughout a company. Once this common ground is established, misunderstandings can be avoided, and the business then has the freedom to pursue organizational or technical transformation goals effectively.

At this point, strengthened links between management, IT specialists, and a process-aware workforce mean more informed decisions become the norm. A successful pairing of process management, enterprise architecture, and IT gives insight into how changes in any one area impact the others, ultimately resulting in a clearer understanding of how the organization actually functions. This translates into an easier path to optimized business processes, and therefore a corresponding improvement in customer satisfaction.

Effective enterprise architecture provides greater transparency inside IT teams, and allows for efficient management of IT systems and their respective interfaces. Along with planning continual IT landscape development, EA supports strategic development of an organization’s structure, just as process management does.

Combining the two leads to a quantum leap in the efficiency and effectiveness of IT systems and business processes, and locks them into a mutually-reinforcing cycle of optimization, meaning improvements will continue over time. Both user communities can contribute to creating a better understanding using a common tool, and the synergy created from joining EA and business process management adds immediate value by driving positive changes company-wide.

Want to find out more? Put on your 3D glasses, and test your EA initiatives with Signavio! Sign up for your free 30-day trial of the Signavio Business Transformation Suite today.

Signavio

Process Paradise by the Dashboard Light

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The right questions drive business success. Questions like, “How can I make sure my product is the best of its kind?” “How can I get the edge over my competitors?” and “How can I keep growing my organization?” Modern businesses take their questions further, focusing on the details of how they actually function. At this level, the questions become, “How can I make my business as efficient as possible?” “How can I improve the way my company does business?” and even, “Why aren’t my company’s processes working as they should?”

Read this article in German:

Mit Dashboards zur Prozessoptimierung

To discover the answers to these questions (and many others!), more and more businesses are turning to process mining. Process mining helps organizations unlock hidden value by automatically collecting information on process models from across the different IT systems operating within a business. This allows for continuous monitoring of an organization’s end-to-end process landscape, meaning managers and staff gain specific operational insights into potential risks—as well as ongoing improvement opportunities.

However, process mining is not a silver bullet that turns data into insights at the push of a button. Process mining software is simply a tool that produces information, which then must be analyzed and acted upon by real people. For this to happen, the information produced must be available to decision-makers in an understandable format.

For most process mining tools, the emphasis remains on the sophistication of analysis capabilities, with the resulting data needing to be interpreted by a select group of experts or specialists within an organization. This necessarily creates a delay between the data being produced, the analysis completed, and actions taken in response.

Process mining software that supports a more collaborative approach by reducing the need for specific expertise can help bridge this gap. Only if hypotheses, analysis, and discoveries are shared, discussed, and agreed upon with a wide range of people can really meaningful insights be generated.

Of course, process mining software is currently capable of generating standardized reports and readouts, but in a business environment where the pace of change is constantly increasing, this may not be sufficient for very much longer. For truly effective process mining, the secret to success will be anticipating challenges and opportunities, then dealing with them as they arise in real time.

Dashboards of the future

To think about how process mining could improve, let’s consider an analog example. Technology evolves to make things easier—think of the difference between keeping track of expenditure using a written ledger vs. an electronic spreadsheet. Now imagine the spreadsheet could tell you exactly when you needed to read it, and where to start, as well as alerting you to errors and omissions before you were even aware you’d made them.

Advances in process mining make this sort of enhanced assistance possible for businesses seeking to improve the way they work. With the right process mining software, companies can build tailored operational cockpits that unite real-time operational data with process management. This allows for the usual continuous monitoring of individual processes and outcomes, but it also offers even clearer insights into an organization’s overall process health.

Combining process mining with an organization’s existing process models in the right way turns these models from static representations of the way a particular process operates, into dynamic dashboards that inform, guide and warn managers and staff about problems in real time. And remember, dynamic doesn’t have to mean distracting—the right process mining software cuts into your processes to reveal an all-new analytical layer of process transparency, making things easier to understand, not harder.

As a result, business transformation initiatives and other improvement plans and can be adapted and restructured on the go, while decision-makers can create automated messages to immediately be advised of problems and guided to where the issues are occurring, allowing corrective action to be completed faster than ever. This rapid evaluation and response across any process inefficiencies will help organizations save time and money by improving wasted cycle times, locating bottlenecks, and uncovering non-compliance across their entire process landscape.

Dynamic dashboards with Signavio

To see for yourself how the most modern and advanced process mining software can help you reveal actionable insights into the way your business works, give Signavio Process Intelligence a try. With Signavio’s Live Insights, all your process information can be visualized in one place, represented through a traffic light system. Simply decide which processes and which activities within them you want to monitor or understand, place the indicators, choose the thresholds, and let Signavio Process Intelligence connect your process models to the data.

Banish multiple tabs and confusing layouts, amaze your colleagues and managers with fact-based insights to support your business transformation, and reduce the time it takes to deliver value from your process management initiatives. To find out more about Signavio Process Intelligence, or sign up for a free 30-day trial, visit www.signavio.com/try.

Process mining is a powerful analysis tool, giving you the visibility, quantifiable numbers, and information you need to improve your business processes. Would you like to read more? With this guide to managing successful process mining initiatives, you will learn that how to get started, how to get the right people on board, and the right project approach.

Savaram Ravindra

How to Ensure Data Quality in an Organization?

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Introduction to Data Quality

Today, the world is filled with data. It is everywhere. And, the value of any organization can be measured by the quality of its data. So, what actually is the quality of data or data quality, and why is it important? Well, data quality refers to the capability of a set of data to serve an intended purpose. 

Data quality is important to any organization because it provides timely and accurate information to manage accountability and services. It also helps to ensure and prioritize the best use of resources. Thus, high-quality data will lead to appropriate insights and valuable information for any organization. We can evaluate the quality of data in certain aspects. They include accuracy, relevancy, completeness, and uniqueness. 

Data Quality Problems

As the organizations are collecting vast amounts of data, managing its quality becomes more important every single day. In the year 2016, the costs of problems caused due to poor data quality were estimated by IBM, and it turned out to be $3.1 trillion across the U.S economy. Also, a Forrester report has stated that almost 30 percent of analysts spend 40 percent of their time validating and vetting their data prior to its utilization for strategic decision-making. These statistics indicate that the scale of the problems with data quality is vast.

So, why do these data quality problems occur? The main reasons include manual entry of data, software updates, integration of data sources, skills shortages, and insufficient testing time. Wrong decisions can be taken due to poor data management processes and poor quality of data. Because of this, many organizations lose their clients and customers. So, ensuring data quality must be given utmost importance in an organization. 

How to Ensure Data Quality?

Data quality management helps by combining data, technology, and organizational culture to deliver useful and accurate results. Good management of data quality builds a foundation for all the initiatives of a business. Now, let’s see how we can improve the data quality in an organization.

The first aspect of improving the quality of data is monitoring and cleansing data. This verifies data against standard statistical measures, validates data against matching descriptions, and uncovers relationships. This also checks the uniqueness of data and analyzes the data for its reusability. 

The second one is managing metadata centrally. Multiple people gather and clean data very often and they may work in different countries or offices. Therefore, you require clear policies on how data is gathered and managed as people in different parts of a company may misinterpret certain data terms and concepts. Centralized management of metadata is the solution to this problem as it reduces inconsistent interpretations and helps in establishing corporate standards.  

The next one is to ensure all the requirements are available and offer documentation for data processors and data providers. You have to format the specifications and offer a data dictionary and also provide training for the providers of data and all other new staff. Make sure you offer immediate help for all the data providers.

Very often, data is gathered from different sources and may include distinct spelling options. Hence, segmentation, scoring, smart lists, and many others are impacted by this. So, for entering a data point, a singular approach is essential, and data normalization provides this approach. The goal of this approach is to eliminate redundancy in data. Its advantages include easier object-to-data mapping and increased consistency.

The last aspect is to verify whether the data is consistent with the data rules and business goals, and this has to be done at regular intervals. You have to communicate the current status and data quality metrics to every stakeholder regularly to ensure the maintenance of data quality discipline across the organization.

Conclusion

Data quality is a continuous process but not a one-time project which needs the entire company to be data-focused and data-driven. It is much more than reliability and accuracy. High level of data quality can be achieved when the decision-makers have confidence in data and rely upon it. Follow the above-mentioned steps to ensure a high level of data quality in your organization. 

Signavio

The Power of Analyzing Processes

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Are you thinking BIG enough? Over the past few years, the quality of discussion regarding a ‘process’ and its interfaces between different departments has developed radically. Organizations increasingly reject guesswork, individual assessments, or blame-shifting and instead focus on objective facts: the display of throughput times, process variants, and their optimization.

But while data can hold valuable insights into business, users, customer bases, and markets, companies are sometimes unsure how best to analyze and harness their data. In fact, the problem isn’t usually a lack of data; it’s a breakdown in leveraging useful data. Being unsure how to interpret, explore, and analyze processes can paralyze any go-live, leading to a failure in the efficient interaction of processes and business operations. Without robust data analysis, your business could be losing money, talent, and even clients.

After all, analyzing processes is about letting data tell its true story for improved understanding.

The “as-is” processes

Analyzing the as-is current state helps organizations document, track, and optimize processes for better performance, greater efficiency, and improved outcomes. By contextualizing data, we gain the ability to navigate and organize processes to negate bottlenecks, set business preferences, and plan an optimized route through process mining initiatives. This focus can help across an entire organization, or on one or more specific processes or trends within a department or team.

There are several vital goals/motivations for implementing current state analysis, including:

  • Saving money and improving ROI;
  • Improving existing processes or creating new processes;
  • Increasing customer satisfaction and journeys;
  • Improving business coordination and organizational responsiveness;
  • Complying with new regulatory standards;
  • Adapting methods following a merger or acquisition.

The “to-be” processes

Simply put, if as-is maps where your processes are, to-be maps where you want them to… be. To-be process mapping documents what you want the process to look like, and by using the as-is diagram, you can work with stakeholders to identify developments and improvements of the current process, then outline those changes on your to-be roadmap.

This analysis can help you make optimal decisions for your business and innovative OpEx imperatives. For instance, at leading data companies like Google and Amazon, data is used in such a way that the analysis results make the decisions! Just think of the power Recommendation Engines, PageRank, and Demand Forecasting Systems have over the content we see. To achieve this, advanced techniques of machine learning and statistical modeling are applied, resulting in mechanically improved results from the data. Interestingly, because these techniques reference large-scale data sets and reflect analysis and results in real-time, they are applied to areas that extend beyond human decision-making.

Also, by analyzing and continuously monitoring qualitative and quantitative data, we gain insights across potential risks and ongoing improvement opportunities, too. The powerful combination of process discovery, process analysis, and conformance checking supports a collaborative approach to process improvement, giving you game-changing insights into your business. For example:

  • Which incidents would I like to detect and act upon proactively?
  • Where would task prioritization help improve overall performance?
  • Where do I know that increased transparency would help the company?
  • How can I utilize processes in place of gut feeling/experience?

Further, as the economic environment continues to change rapidly, and modern organizations keep adopting process-based approaches to ensure they are achieving their business goals, process analysis naturally becomes the perfect template for any company.

With this, process mining technology can help modern businesses manage process challenges beyond the boundaries of implementation. We can evaluate the proof of concept (PoC) for any proposed improvements, and extract relevant information from a homogenous data set. Of course, process modeling and business process management (BPM) are available to solve the potentially tricky integration phase.

Process mining and analysis initiatives

Process mining and discovery initiatives can also provide critical insights throughout the automation and any Robotic Process Automation (RPA) journey, from defining the strategy to continuous improvement and innovation. Data-based process mining can even extend process analysis across teams and individuals, decreasing incident resolution times, and subsequently improving working habits via the discovery and validation of automation opportunities.

A further example of where process mining and strategic process analysis/alignment is already paying dividends is IT incident management. Here, “incident” is an unplanned interruption to an IT service, which may be complete unavailability or merely a reduction in quality. The goal of the incident management process is to restore regular service operation as quickly as possible and to minimize the impact on business operations. Incident management is a critical process in Information Technology Library (ITIL).

Process mining can also further drive improvement in as-is incident management processes as well as exceptional and unwanted process steps, by increasing visibility and transparency across IT processes. Process mining will swiftly analyze the different working habits across teams and individuals, decreasing incident resolution times, and subsequently improving customer impact cases.

Positive and practical experiences with process mining across industries have also led to the further dynamic development of tools, use cases, and the end-user community. Even with very experienced process owners, the visualization of processes can skyrocket improvement via new ideas and discussion.

However, the potential performance gains are more extensive, with the benefits of using process mining for incident management, also including:

  • Finding out how escalation rules are working and how the escalation is done;
  • Calculating incident management KPIs, including SLA (%);
  • Discovering root causes for process problems;
  • Understanding the effect of the opening interface (email, web form, phone, etc.);
  • Calculating the cost of the incident process;
  • Aligning the incident management system with your incident management process.

Robotic Process Automation (RPA)

Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive, and error-prone tasks. However, successful process automation requires specific knowledge about the intended (and potential) benefits, effective training of the robots, and continuous monitoring of their performance and processes.

With this, process mining supports organizations throughout the lifecycle of RPA initiatives by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process miners and managers with a particular interest in process automation. By unlocking the experiences with process mining, a company better understands what is needed today, for tomorrow’s process initiatives.

To further upgrade the impact of robot-led automation, there is also a need for a solid understanding of legacy systems, and an overview of automation opportunities. Process mining tools provide key insights throughout the entire RPA journey, from defining the strategy to continuous improvement and innovation.

Benefits of process mining and analysis within the RPA lifecycle include:

  1. Overviews of processes within the company, based on specific criteria;
  2. Identification of processes suitable for RPA implementation during the preparation phase;
  3. Mining the optimal process flow/process path;
  4. Understanding the extent to which RPA can be implemented in legacy processes and systems;
  5. Monitoring and analysis of RPA performance during the transition/handover of customization;
  6. Monitoring and continuous improvement of RPA in the post-implementation phase.

The process of better business understanding

Every organization is different and brings with it a variety of process-related questions. Yet some patterns are usually repeated. For example, customers who introduce data supported process analysis as part of business transformation initiatives will typically face challenges in harmonizing processes from fragmented sectors and regional locations. Here it helps enormously to base actions on data and statistics from the respective processes, instead of relying on the instincts and estimations of individuals.

With this, process analysis which is supported by data, enables a fact-based discussion, and builds a bridge between employees, process experts and management. This helps avoid siloed thinking, as well as allowing the transparent design of handovers and process steps which cross departmental boundaries within an organization.

In other words, to unlock future success and transformation, we must be processing… today.

Find out more about process mining with Signavio Process Intelligence, and see how it can help your organization uncover the hidden value of process, generate fresh ideas, and save time and money.

Signavio

From BI to PI: The Next Step in the Evolution of Data-Driven Decisions

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“Change is a constant.” “The pace of change is accelerating.” “The world is increasingly complex, and businesses have to keep up.” Organizations of all shapes and sizes have heard these ideas over and over—perhaps too often! However, the truth remains that adaptation is crucial to a successful business.

Read this article in German: Von der Datenanalyse zur Prozessverbesserung: So gelingt eine erfolgreiche Process-Mining-Initiative

 

Of course, the only way to ensure that the decisions you make are evolving in the right way is to understand the underlying building blocks of your organization. You can think of it as DNA; the business processes that underpin the way you work and combine to create a single unified whole. Knowing how those processes operate, and where the opportunities for improvement lie, can be the difference between success and failure.

Businesses with an eye on their growth understand this already. In the past, Business Intelligence was seen as the solution to this challenge. In more recent times, forward-thinking organizations see the need for monitoring solutions that can keep up with today’s rate of change, at the same time as they recognize that increasing complexity within business processes means traditional methods are no longer sufficient.

Adapting to a changing environment? The challenges of BI

Business Intelligence itself is not necessarily defunct or obsolete. However, the tools and solutions that enable Business Intelligence face a range of challenges in a fast-paced and constantly changing world. Some of these issues may include:

  • High data latency – Data latency refers to how long it takes for a business user to retrieve data from, for example, a business intelligence dashboard. In many cases, this can take more than 24 hours, a critical time period when businesses are attempting to take advantage of opportunities that may have a limited timeframe.
  • Incomplete data sets – The broad approach of Business Intelligence means investigations may run wide but not deep. This increases the chances that data will be missed, especially in instances where the tools themselves make the parameters for investigations difficult to change.
  • Discovery, not analysis – Business intelligence tools are primarily optimized for exploration, with a focus on actually finding data that may be useful to their users. Often, this is where the tools stop, offering no simple way for users to actually analyze the data, and therefore reducing the possibility of finding actionable insights.
  • Limited scalability – In general, Business Intelligence remains an arena for specialists and experts, leaving a gap in understanding for operational staff. Without a wide appreciation for processes and their analysis within an organization, the opportunities to increase the application of a particular Business Intelligence tool will be limited.
  • Unconnected metrics – Business Intelligence can be significantly restricted in its capacity to support positive change within a business through the use of metrics that are not connected to the business context. This makes it difficult for users to interpret and understand the results of an investigation, and apply these results to a useful purpose within their organization.

Process Intelligence: the next evolutionary step

To ensure companies can work efficiently and make the best decisions, a more effective method of process discovery is needed. Process Intelligence (PI) provides the critical background to answer questions that cannot be answered with Business Intelligence tools.

Process Intelligence offers visualization of end-to-end process sequences using raw data, and the right Process Intelligence tool means analysis of that raw data can be conducted straight away, so that processes are displayed accurately. The end-user is free to view and work with this accurate information as they please, without the need to do a preselection for the analysis.

By comparison, because Business Intelligence requires predefined analysis criteria, only once the criteria are defined can BI be truly useful. Organizations can avoid delayed analysis by using Process Intelligence to identify the root causes of process problems, then selecting the right criteria to determine the analysis framework.

Then, you can analyze your system processes and see the gaps and variants between the intended business process and what you actually have. And of course, the faster you discover what you have, the faster you can apply the changes that will make a difference in your business.

In short, Business Intelligence is suitable for gaining a broad understanding of the way a business usually functions. For some businesses, this will be sufficient. For others, an overview is not enough.

They understand that true insights lie in the detail, and are looking for a way of drilling down into exactly how each process within their organization actually works. Software that combines process discovery, process analysis, and conformance checking is the answer.

The right Process Intelligence tools means you will be able to automatically mine process models from the different IT systems operating within your business, as well as continuously monitor your end-to-end processes for insights into potential risks and ongoing improvement opportunities. All of this is in service of a collaborative approach to process improvement, which will lead to a game-changing understanding of how your business works, and how it can work better.

Early humans evolved from more primitive ancestors, and in the process, learned to use more and more sophisticated tools. For the modern human, working in a complex organization, the right tool is Process Intelligence.

Endless Potential with Signavio Process Intelligence

Signavio Process Intelligence allows you to unearth the truth about your processes and make better decisions based on true evidence found in your organization’s IT systems. Get a complete end-to-end perspective and understanding of exactly what is happening in your organization in a matter of weeks.

As part of Signavio Business Transformation Suite, Signavio Process Intelligence integrates perfectly with Signavio Process Manager and is accessible from the Signavio Collaboration Hub. As an entirely cloud-based process mining solution, the tool makes it easy to collaborate with colleagues from all over the world and harness the wisdom of the crowd.

Find out more about Signavio Process Intelligence, and see how it can help your organization generate more ideas, save time and money, and optimize processes.

Benjamin Aunkofer

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

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Interview with Ross Perez, Senior Director, Marketing EMEA at Snowflake

Read this article in German:
“Profitiert Business Intelligence vom Data Warehouse in der Cloud?”

Does Business Intelligence benefit from Cloud Data Warehousing?

Ross Perez is the Senior Director, Marketing EMEA at Snowflake. He leads the Snowflake marketing team in EMEA and is charged with starting the discussion about analytics, data, and cloud data warehousing across EMEA. Before Snowflake, Ross was a product marketer at Tableau Software where he founded the Iron Viz Championship, the world’s largest and longest running data visualization competition.

Data Science Blog: Ross, Business Intelligence (BI) is not really a new trend. In 2019/2020, making data available for the whole company should not be a big thing anymore. Would you agree?

BI is definitely an old trend, reporting has been around for 50 years. People are accustomed to seeing statistics and data for the company at large, and even their business units. However, using BI to deliver analytics to everyone in the organization and encouraging them to make decisions based on data for their specific area is relatively new. In a lot of the companies Snowflake works with, there is a huge new group of people who have recently received access to self-service BI and visualization tools like Tableau, Looker and Sigma, and they are just starting to find answers to their questions.

Data Science Blog: Up until today, BI was just about delivering dashboards for reporting to the business. The data warehouse (DWH) was something like the backend. Today we have increased demand for data transparency. How should companies deal with this demand?

Because more people in more departments are wanting access to data more frequently, the demand on backend systems like the data warehouse is skyrocketing. In many cases, companies have data warehouses that weren’t built to cope with this concurrent demand and that means that the experience is slow. End users have to wait a long time for their reports. That is where Snowflake comes in: since we can use the power of the cloud to spin up resources on demand, we can serve any number of concurrent users. Snowflake can also house unlimited amounts of data, of both structured and semi-structured formats.

Data Science Blog: Would you say the DWH is the key driver for becoming a data-driven organization? What else should be considered here?

Absolutely. Without having all of your data in a single, highly elastic, and flexible data warehouse, it can be a huge challenge to actually deliver insight to people in the organization.

Data Science Blog: So much for the theory, now let’s talk about specific use cases. In general, it matters a lot whether you are storing and analyzing e.g. financial data or machine data. What do we have to consider for both purposes?

Financial data and machine data do look very different, and often come in different formats. For instance, financial data is often in a standard relational format. Data like this needs to be able to be easily queried with standard SQL, something that many Hadoop and noSQL tools were unable to provide. Luckily, Snowflake is an ansi-standard SQL data warehouse so it can be used with this type of data quite seamlessly.

On the other hand, machine data is often semi-structured or even completely unstructured. This type of data is becoming significantly more common with the rise of IoT, but traditional data warehouses were very bad at dealing with it since they were optimized for relational data. Semi-structured data like JSON, Avro, XML, Orc and Parquet can be loaded into Snowflake for analysis quite seamlessly in its native format. This is important, because you don’t want to have to flatten the data to get any use from it.

Both types of data are important, and Snowflake is really the first data warehouse that can work with them both seamlessly.

Data Science Blog: Back to the common business use case: Creating sales or purchase reports for the business managers, based on data from ERP-systems such as Microsoft or SAP. Which architecture for the DWH could be the right one? How many and which database layers do you see as necessary?

The type of report largely does not matter, because in all cases you want a data warehouse that can support all of your data and serve all of your users. Ideally, you also want to be able to turn it off and on depending on demand. That means that you need a cloud-based architecture… and specifically Snowflake’s innovative architecture that separates storage and compute, making it possible to pay for exactly what you use.

Data Science Blog: Where would you implement the main part of the business logic for the report? In the DWH or in the reporting tool? Does it matter which reporting tool we choose?

The great thing is that you can choose either. Snowflake, as an ansi-Standard SQL data warehouse, can support a high degree of data modeling and business logic. But you can also utilize partners like Looker and Sigma who specialize in data modeling for BI. We think it’s best that the customer chooses what is right for them.

Data Science Blog: Snowflake enables organizations to store and manage their data in the cloud. Does it mean companies lose control over their storage and data management?

Customers have complete control over their data, and in fact Snowflake cannot see, alter or change any aspect of their data. The benefit of a cloud solution is that customers don’t have to manage the infrastructure or the tuning – they decide how they want to store and analyze their data and Snowflake takes care of the rest.

Data Science Blog: How big is the effort for smaller and medium sized companies to set up a DWH in the cloud? Does this have to be an expensive long-term project in every case?

The nice thing about Snowflake is that you can get started with a free trial in a few minutes. Now, moving from a traditional data warehouse to Snowflake can take some time, depending on the legacy technology that you are using. But Snowflake itself is quite easy to set up and very much compatible with historical tools making it relatively easy to move over.

Benjamin Aunkofer

Business Intelligence Organizations

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I am often asked how the Business Intelligence department should be set up and how it should interact and collaborate with other departments. First and foremost: There is no magic recipe here, but every company must find the right organization for itself.

Before we can talk about organization of BI, we need to have a clear definition of roles for team members within a BI department.

A Data Engineer (also Database Developer) uses databases to save structured, semi-structured and unstructured data. He or she is responsible for data cleaning, data availability, data models and also for the database performance. Furthermore, a good Data Engineer has at least basic knowledge about data security and data privacy. A Data Engineer uses SQL and NoSQL-Technologies.

A Data Analyst (also BI Analyst or BI Consultant) uses the data delivered by the Data Engineer to create or adjust data models and implementing business logic in those data models and BI dashboards. He or she needs to understand the needs of the business. This job requires good communication and consulting skills as well as good developing skills in SQL and BI Tools such like MS Power BI, Tableau or Qlik.

A Business Analyst (also Business Data Analyst) is a person form any business department who has basic knowledge in data analysis. He or she has good knowledge in MS Excel and at least basic knowledge in data analysis and BI Tools. A Business Analyst will not create data models in databases but uses existing data models to create dashboards or to adjust existing data analysis applications. Good Business Analyst have SQL Skills.

A Data Scientist is a Data Analyst with extended skills in statistics and machine learning. He or she can use very specific tools and analytical methods for finding pattern in unknow or big data (Data Mining) or to predict events based on pattern calculated by using historized data (Predictive Analytics). Data Scientists work mostly with Python or R programming.

Organization Type 1 – Central Approach (Data Lab)

The first type of organization is the data lab approach. This organization form is easy to manage because it’s focused and therefore clear in terms of budgeting. The data delivery is done centrally by experts and their method and technology knowledge. Consequently, the quality expectation of data delivery and data analysis as well as the whole development process is highest here. Also the data governance is simple and the responsibilities clearly adjustable. Not to be underestimated is the aspect of recruiting, because new employees and qualified applicants like to join a central team of experts.

However, this form of organization requires that the company has the right working attitude, especially in the business intelligence department. A centralized business intelligence department acts as a shared service. Accordingly, customer-oriented thinking becomes a prerequisite for the company’s success – and customers here are the other departments that need access to the capacities of those centralized data experts. Communication boundaries must be overcome and ways of simple and effective communication must be found.

Organization Type 2 – Stakeholder Focus Approach

Other companies want to shift more responsibility for data governance, and especially data use and analytics, to those departments where data plays a key role right now. A central business intelligence department manages its own projects, which have a meaning for the entire company. The specialist departments, which have a special need for data analysis, have their own data experts who carry out critical projects for the specialist department. The central Business Intelligence department does not only provide the technical delivery of data, but also through methodical consulting. Although most of the responsibility lies with the Business Intelligence department, some other data-focused departments are at least co-responsible.

The advantage is obvious: There are special data experts who work deeper in the actual departments and feel more connected and responsible to them. The technical-business focus lies on pain points of the company.

However, this form of Ogranization also has decisive disadvantages: The danger of developing isolated solutions that are so special in some specific areas that they will not really work company-wide increases. Typically the company has to deal with asymmetrical growth of data analytics
know-how. Managing data governance is more complex and recruitment is becoming more difficult as the business intelligence department is weakened and smaller, and data professionals for other departments need to have more business focus, which means they are looking for more specialized profiles.

Organization Type 3 – Decentral Approach

Some companies are also taking a more extreme approach in the other direction. The Business Intelligence department now has only Data Engineers building and maintaining the data warehouse or data lake. As a result, the central department only provides data; it is used and analyzed in all other departments, specifically for the respective applications.

The advantage lies in the personal responsibility of the respective departments as „pain points“ of the company are in focus in belief that business departments know their problems and solutions better than any other department does. Highly specialized data experts can understand colleagues of their own department well and there is no no shared service mindset neccessary, except for the data delivery.

Of course, this organizational form has clear disadvantages since many isolated solutions are unavoidable and the development process of each data-driven solution will be inefficient. These insular solutions may work with luck for your own department, but not for the whole company. There is no one single source of truth. The recruiting process is more difficult as it requires more specialized data experts with more business background. We have to expect an asymmetrical growth of data analytics know-how and a difficult data governance.

 

Kyle Weller

Bringing intelligence to where data lives: Python & R embedded in T-SQL

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Introduction

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. Train and store machine learning models in your database bringing intelligence to where your data lives.

You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server. We also offer leading edge, high-performance algorithms in Microsoft’s RevoScaleR and RevoScalePy APIs. Using these with the latest innovations in the open source world allows you to bring unparalleled selection, performance, and scale to your applications.

If you are excited to try out SQL Server Machine Learning Services, check out the hands on tutorial below. If you do not have Machine Learning Services installed in SQL Server,you will first want to follow the getting started tutorial I published here: 

How-To Tutorial

In this tutorial, I will cover the basics of how to Execute R and Python in T-SQL statements. If you prefer learning through videos, I also published the tutorial on YouTube.

Basics

Open up SQL Server Management Studio and make a connection to your server. Open a new query and paste this basic example: (While I use Python in these samples, you can do everything with R as well)

EXEC sp_execute_external_script @language = N'Python',
@script = N'print(3+4)'

Sp_execute_external_script is a special system stored procedure that enables R and Python execution in SQL Server. There is a “language” parameter that allows us to choose between Python and R. There is a “script” parameter where we can paste R or Python code. If you do not see an output print 7, go back and review the setup steps in this article.

Parameter Introduction

Now that we discussed a basic example, let’s start adding more pieces:

EXEC sp_execute_external_script  @language =N'Python', 
@script = N' 
OutputDataSet = InputDataSet;
',
@input_data_1 =N'SELECT 1 AS Col1';

Machine Learning Services provides more natural communications between SQL and R/Python with an input data parameter that accepts any SQL query. The input parameter name is called “input_data_1”.
You can see in the python code that there are default variables defined to pass data between Python and SQL. The default variable names are “OutputDataSet” and “InputDataSet” You can change these default names like this example:

EXEC sp_execute_external_script  @language =N'Python', 
@script = N' 
MyOutput = MyInput;
',
@input_data_1_name = N'MyInput',
@input_data_1 =N'SELECT 1 AS foo',
@output_data_1_name =N'MyOutput';

As you executed these examples, you might have noticed that they each return a result with “(No column name)”? You can specify a name for the columns that are returned by adding the WITH RESULT SETS clause to the end of the statement which is a comma separated list of columns and their datatypes.

EXEC sp_execute_external_script  @language =N'Python', 
@script=N' 
MyOutput = MyInput;
',
@input_data_1_name = N'MyInput',
@input_data_1 =N'
SELECT 1 AS foo,
2 AS bar
',
@output_data_1_name =N'MyOutput'
WITH RESULT SETS ((MyColName int, MyColName2 int));

Input/Output Data Types

Alright, let’s discuss a little more about the input/output data types used between SQL and Python. Your input SQL SELECT statement passes a “Dataframe” to python relying on the Python Pandas package. Your output from Python back to SQL also needs to be in a Pandas Dataframe object. If you need to convert scalar values into a dataframe here is an example:

EXEC sp_execute_external_script  @language =N'Python', 
@script=N' 
import pandas as pd
c = 1/2
d = 1*2
s = pd.Series([c,d])
df = pd.DataFrame(s)
OutputDataSet = df
'

Variables c and d are both scalar values, which you can add to a pandas Series if you like, and then convert them to a pandas dataframe. This one shows a little bit more complicated example, go read up on the python pandas package documentation for more details and examples:

EXEC sp_execute_external_script  @language =N'Python', 
@script=N' 
import pandas as pd
s = {"col1": [1, 2], "col2": [3, 4]}
df = pd.DataFrame(s)
OutputDataSet = df
'

You now know the basics to execute Python in T-SQL!

Did you know you can also write your R and Python code in your favorite IDE like RStudio and Jupyter Notebooks and then remotely send the execution of that code to SQL Server? Check out these documentation links to learn more: https://aka.ms/R-RemoteSQLExecution https://aka.ms/PythonRemoteSQLExecution

Check out the SQL Server Machine Learning Services documentation page for more documentation, samples, and solutions. Check out these E2E tutorials on github as well.

Would love to hear from you! Leave a comment below to ask a question, or start a discussion!