What Customers Want from Business Transformation Solutions

No matter what industry you work in, customer satisfaction is paramount. After all, in most cases,  there can be no business without customers! As such, keeping customers happy is a driving force for many organizations. All sorts of studies have been conducted, articles and reports have been drafted and re-drafted, and consumers bombarded with market research questions, all in search of understanding what customers want, and how businesses can offer it to them. 


Read this article in German:

Was Kunden von Business-Transformation-Lösungen erwarten

 


From a process management perspective, the technology to track the way customers interact with your business already exists, in the form of customer journey mapping (CJM). Customer journey mapping helps you to understand exactly how customers engage with your business, and what their experience is like when they do. It helps answer questions like: 

  • Do customers have positive or negative feelings when they interact with specific touchpoints within your organization’s processes? 
  • Are there points where customers stall, or disengage, or want more information? 
  • How do the people you’re trying to reach really respond to your customer service options?

Asking these questions internally is essential. However, an even more vital tool in building customer satisfaction and loyalty is a very simple one: just ask! 

What business transformation customers want

Technology enables businesses to ask customers directly about products and services more easily than ever, but there is an associated risk of consulting customers too much. Rather than letting customers know you care, this can instead make them feel the opposite. In addition, restrictions on the collection and use of customer data means actually contacting customers can be a challenge.

One way to overcome this is to make use of one of the technical review services available online. Browsing any of these sites reveals a wealth of information about what customers value in all sorts of industries. For example, Signavio uses IT Central Station to track customer views on business transformation software. When we consider these views in aggregate, we can see two common threads emerge over and over: collaboration and ease of use.

Comments from real users return to these points often:

  • “For me, the features I find most valuable are definitely in the Collaboration Hub. We are getting more users on there and becoming more familiar with it.”
  • “Based on my experience, one of the best features offered by Signavio is its simple Collaboration Hub functions, where users from various departments can constantly refer to their TO-BE process design.”
  • One of the important things for us, when we were looking at solutions, was the ease of use. The ease of use affected the adoption in our organization massively. If it had not been easy to use and people were struggling with it, then they just would not have used it. So I’d say it’s quite a high factor in making a choice.”
  • “One of the most valuable features is ease of use, which has really been a good thing to put into the business. People like tools that they can just pick up and use straight-away.”
  • “The interface is quite intuitive. I am modeling a lot of processes, so for me, it’s quite easy.”

A final piece of advice

Knowing where customers find value is crucial to understanding how to meet their needs best, and thus creating ongoing and meaningful customer relationships. As with many customer-focused issues, feelings play a large role. 

The same can be said for business transformation, as the Lead Business Analyst at a media company with over 10,000 employees pointed out: “You will have a gut feel of what you want to do and when you actually look at the tools that are out there it is easier to make your decision.”

If you’re ready to make your decision about the right business transformation solution for you, register for a free 30-day trial with Signavio today.

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

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.

New Sponsor: Snowflake

Dear readers,

we have good news again: Now we welcome snowflake as our new Data Science Blog Sponsor! So we are booked out for the moment regarding sponsoring. Snowflake provides data warehousing for the cloud and has an unique data, access and feature model, the snowflake. Now we are looking forward to editorial contributions by snowflake.

Snowflake is the only data warehouse built for the cloud. Snowflake delivers the performance, concurrency and simplicity needed to store and analyze all data available to an organization in one location. Snowflake’s technology combines the power of data warehousing, the flexibility of big data platforms, the elasticity of the cloud, and live data sharing at a fraction of the cost of traditional solutions. Snowflake: Your data, no limits. Find out more at snowflake.net.

Furthermore, snowflake will also sponsor our Data Leader Days 2018 in November in Berlin!

New Sponsor: Cloudera

Dear readers,

we have good news: We welcome Cloudera as our new Data Science Blog Sponsor! Cloudera is one of the most famous platform and solution provider for big data analytics and machine learning. This also means editorial contributions by Cloudera for at least one year.

At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower people to transform complex data into clear and actionable insights. We deliver the modern platform for machine learning and analytics optimized for the cloud. The world’s largest enterprises trust Cloudera to help solve their most challenging business problems.

Learn more about our new sponsor at cloudera.com.

New Sponsor: lexoro.ai

We wish our readers a happy new year and have good news: We welcome lexoro as our new Data Science Blog Sponsor for 2018!

lexoro GmbH is a Talent Management and Consulting company in the cosmos of the broad topic of Artificial Intelligence. Our focus lies on the relevant technologies and trends in the fields of data science, machine learning and big data. We identify and connect the best talents and experts behind the buzzwords, and help technology-focused industrial and consulting firms in finding the right people with the right skills to build and grow their analytics teams. In addition, we advise companies in identifying their individual challenges, hurdles and opportunities that go along with the great hype of Artificial Intelligence. We develop A.I. Prototypes and make the market transparent with industry-typical use cases.

Do you want to know more about lexoro? Visit them on lexoro.ai!