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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

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.

Lisa Sagal

OLAP Technology in Business Intelligence

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Data in Business Intelligence
Business processes traditionally comprise three stages of data management: collecting, analyzing, and reporting. First, data should be gathered from all the sources through ETL tools (Extract, Transform, Load). After this, there are often issues occurring connected with data consistency hence the data should be cleaned and structured using the function of metadata. Once the data are provided to the end-user in a readable and transparent way it is ready to be analyzed. There are multiple applications ensuring data analysis including Data Mining, OLAP, BI. In order to carry out in-depth and coherent analysis, the best approach is to initially determine KPI as these are the criteria to assess the progress in relation to the goals set.

OLAP definition
OLAP tool belongs to Business Intelligence concept intended for big data management and is short for Online Analytical Processing. OLAP conducts multidimensional data analysis and enables end-users to perform complicated calculations, trend analysis, ‘what-if’ scenarios and the like. Furthermore, owing to OLAP it’s possible to conduct planning and forecasting, budgeting and financial reporting, analysis, and data modeling which contributes to successful decision making in business.

OLAP Structure
An OLAP cube is composed of dimensions containing aggregated information referred to and measures which include numerical data. Dimensions are arranged in hierarchies which in their turn are indicators to determine the rate of granularity; the rate is called a level. The most common dimensions are location, product, and time. The lowest granularity level of a time dimension may be hours while the highest one can present years. This way when there is a query to be responded the measures contribute to filter out the data and select the right object inside the dimension. In the center of the cube there is a star or a snowflake schema which all the dimensions refer to.

OLAP main characteristics
Here are the main features characterizing the OLAP tool”:

– The data in OLAP is structured as a multidimensional cube.
– The cube structure allows users to see the information from various angles given location, products, demographics, time, etc.
– Rapid data access and analysis due to precalculated aggregations.
– Simple and intuitive interface.
– OLAP doesn’t require IT skills or SQL knowledge (as some other business intelligence software tools). Hence its operation eases the burden of IT department.
– The tool supports complex custom calculations
– The OLAP databases maintain historical data and are updated not constantly but regularly.
– The cube design and building process is the pivotal step on the way to successful data processing.

OLAP requirements
When the OLAP technology was invented there were twelve rules generated to follow so that it complies with the concept of online data processing:

Multidimensional
Not only the OLAP view has to be multidimensional but the data should as well be stored in this way of structure in order to provide the multidimensional analysis.

Transparent
The architecture has to be transparent to let the user see and understand the functionality and the client server of the application.

Accessible
The end user must have an opportunity to access the information in its consistent view without any issues related to the sources where the data come from or the way the data are maintained in OLAP.

Consistent Reporting
The data are regularly upgraded and its volume grows progressively although the user shouldn’t see problems changes in the process of scheduled reporting regarding that.

Client-Server
OLAP application has to manage client-server architecture as it manages vast volumes of data often requiring a core server for storage and maintenance.

Common Dimensionality
The main feature of the dimension structure in OLAP must be the same for all the dimensions to keep the data consistent, accurate, valid, complete, etc. Thus the dimensions have to possess common operation capabilities and be equal in structure.

Dynamic Sparse Matrix Handling
A usual OLAP application must manage to deal with sparse matrices and shouldn’t let the cube expand excessively as a usual OLAP cube is relatively sparse.

Multi-User
OLAP technology is originally supposed to provide an opportunity to access the data for multiple users simultaneously. The process of data management must at the same time be ensured with security and integrity.

Unrestricted Cross-dimensional Operations
A typical OLAP application is meant to handle all calculations and operations (such as slice-and-dice, drill up-down, drill through etc.) without the participation of the user. Commonly the tool delivers a language to exploit while requiring specified information.

Intuitive Data Manipulation
All OLAP operations which handle dimensions, measures, hierarchies, levels etc. have to be user-friendly and easily adopted without requiring additional technical skills. An average employee is considered to cope with the data navigation and management through clear displaying and handy operations.

Flexible Reporting
The main function – reporting must be flexible with a view to organizing all the rows, columns, and page setup containing a requisite number of dimensions and hierarchies from the data. As a result, the user has to gain a report comprising all the needed members and the relations between them.

Unlimited Dimensions and Aggregation Levels
When the technology was designed it was intended to be able to contain up to twenty dimensions in the cube. Each dimension had to provide as many aggregation levels inside a hierarchy as required. The idea was to manage great volumes of data keeping end-users absolutely aware of the performance of the organization.

Advantages of OLAP
Speed
Before OLAP was invented and introduced to the market there hadn’t been a tool to rapidly run the queries and it had taken long to retrieve the required information from the data. Thus the main advantage of the OLAP application is its speed gained due to precomputation of the data aggregations.

MDX designer and ad-hoc reports
MDX Designer is aimed at creating interactive ad-hoc reports. The reports provide a better understanding of the business processes and the organization’s performance in the market.

Visualization
OLAP provides its users with sophisticated data analytics allowing them to see data from different perspectives. There are numerous formats to visualize the requisite data: pie charts, graphs, heat maps, reports, pyramids, etc. Moreover, OLAP includes a number of operations to handle data: rotate, drill up and down, slice and dice, etc. Besides, there’s also an opportunity to apply a ‘what-if’ scenario due to a write-back option. All mentioned above can significantly contribute to decision-making process regarding the ongoing situation.

Flexibility
OLAP table displayed is flexible with column and row labels depending on the requirements of the user. Moreover, the reporting generated is available in multiple dimensions.