5.2 HD-business and the fundamental role of data

I have insisted on the central role of current and correct data for business in the digital age multiple times in this blog. As all of this sounds (and in fact is) very technical I want to illustrate what I have in mind with another comparison.

Think of the very first black & white televisions in the middle of the last century. The pictures oftentimes were blurry and the sound quality was poor. Now compare them to today’s state-of-the-art devices with high definition pictures, surround sound and interactive second screen features. And all of that for an affordable price. Would you want to go back?

 Enterprises need to ask themselves what kind of picture they have from the customers who buy their products and services. In many companies that I know it will look like the one on the left side: blurry, with many information missing – you can’t even guess the gender of some of the faces correctly.

Digital companies strive for the picture on the right: Precise information on customers and their buying behaviors, even anticipating what they want to do next. This is what I call “high definition business”. The point is that you cannot get from left to right without more precise data. Interpolating something will lead to best guesses but there might be a competitor who possesses better data on your customers. And that is where he will attack you.

5.1 Welcome to the future: business benefits

The benefits for the business – and let’s not forget that this is where the all the money comes from – can be separated into two large domains.

1. Solving existing challenges thus running the current business much better
2. Offering new processes and services to customers thus innovate and transform for the (digital) future

 

1. Solving existing challenges

In chapter 2.3 I described a selection of current business challenges in various industries. My claim is that many if not all of them will disappear with the implementation of a Corporate Data Pool. However these challenges are so manifold that companies need to prioritize them according to business needs. We can achieve a lot, even out-of-the-box, but not everything at once. It is important to have the big picture in mind and then to go step-by-step (see also chapter 6 “Transition path”).

Here is a non-exhaustive list of business challenges which can be solved with the approach described here along with their respective business benefits:

• Intransparent status on the overall business situation due to bad data quality (incomplete, outdated, wrong) and missing reporting features – this comes in various shapes: cash management, risk management, fraud detection, churn management, etc., and it has negative business impact in all cases
  >> immediate improvements <<
• Long process durations, slow (annoying) customer interaction due to long system response times
  >> immediate acceleration <<
• Long response times and inadequate measures to unplanned, spontaneous events (like disasters)
  >> immediate improvements <<
• Poor product/service quality due to bad data quality
  >> improvements through consolidation of isolated data silos <<
• High manual work load in processes due to incomplete data and insufficient degree of automation
  >> improvements through consolidation of isolated data silos and usage of built-in features <<
• Inability to match compliance and transparency regulations due to bad data quality
  >> improvements through consolidation of isolated data silos <<
• Inability to offer “one face to the customer” over multiple contact channels
  >> improvements through consolidation of isolated data silos <<

All of the improvements lead to satisfied customers, a high loyality rate and additional revenues. Moreover in the age of social media it takes a few hours to ruin an enterprise's reputation which has been built over many years of customers are dissatisfied.

2. Innovating for the future

As mentioned before in this blog all enterprises face the challenges of digital business. The emerging era builds on correct and current data: the better you can deal with it, the more successful your business will be. It is as simple as that.

Here comes the non-exhaustive list of innovative business scenarios which can be implemented on top of a Corporate Data Pool. As we are talking about innovation it is all about creativity and beating your competitors. And this is exactly the reason why you, dear reader, will come up with many more ideas for your business beyond the buzzwords that I present here:

• Big data analytics: This is the concept of using huge amounts of data to analyze customer behavior in relation to my offerings and get business benefits out of it (e.g. through better products, services)
  >> immediate benefits <<
• Embedded services: One effect of digitalizing value chains is that services that belong to a value chain become easy to reproduce and therefore easy to replace. This is what Alphabet/Google and Apple demonstrate when they attack traditional value chain and business models with their digital platforms and services. Managing payments or the physical transport of goods within a value chain are typical examples for these changes. If you want to remain relevant in your value chain (that means: stay in business) you will need a differentiator (or “USP”) for your services. And in the digital world USP comes from - data.
  >> immediate benefits <<
• Internet of things IoT/Internet of everything: “Things” (sensors, machines, robots, cars, etc.) have started to communicated with each other in order to coordinate their collaboration autonomously. Machines order their own spare parts, cars warn each other of accidents, plants calculate their optimal amount of fertilizer. All of this will have huge rationalization potential – and it will produce even more digital data.
  >> immediate benefits <<
• Industry 4.0: This is a special topic currently discussed in the world of manufacturing. The basic idea is that all physical things will have a certain DNA just like human beings that make them aware of what they are and what their purpose is. “Production” in the sense of creating physical things will not be organized and managed from the outside anymore. Instead every thing will organize its own process: creation, transformation, transportation, replacement, etc. This requires a high degree of digital interaction. And again will produce massive amounts of digital data.
  >> immediate benefits <<
• Predictive/adaptive business: A better response to highly individual customer demands is definitely a great source of business success as demonstrated by leading digital players. It includes the prediction of future customer behavior (whereat “future” can mean anything from weeks to seconds) as well as the flexible adaptation of businesses to it which could eventually lead to a highly individual product or service which is produced exactly one time.
  >> benefits especially when the concept of a Corporate Data Pool is extended beyond the boundaries of the enterprise throughout all participants in a value chain <<
• Adaptive workforce: The way how we organize human labor today is great to provide individual security and reliability to employees and citizens but it is highly inefficient in using the human potential to generate wealth. Real-time data and connected things offers us much more options and freedom to collaborate upon our individual strengths.
  >> This is rather a long-term project as it heavily impacts social topics. A Corporate Data Pool will however facilitate the efficient sharing of human potential with an organization. <<

There is so much more to be mentioned here. Feel free to start a discussion with me.

To summarize the thoughts in this chapter:
The potential of working with data in a different way other than we do today is huge in many respects. When you keep this in mind it should not be hard to find a business case to justify the initial investments which are required to start the journey (we will come to this a little later).

4.3 Corporate Data Pool becomes reality: what does it mean?

You can easily guess the picture when we do the procedure described in chapter 4.2 multiple times: we will end up with a single HANA database for many applications. From now on I will use the following picture to illustrate how a Corporate Data Pool on HANA works and what it means for our IT landscape.

The box on the bottom of the picture represents our corporate HANA server which we will use as single database for numerous applications. Three applications are shown in the picture for reasons of simplification but it could and will be many more. These applications can be anything, even a mobile app, but to evaluate the benefits we are interested in heavy-weight mission-critical applications with significant business logic.

For now we will assume that there exists a second HANA box for all the SAP systems. Technically it is not necessary but SAP will not allow us to take other applications in their dedicated database. Let’s not worry about it for now as this is not really an issue. We handled 500 databases before, we can handle two!

Let’s have a look how the landscape has changed and how we benefit from an IT perspective. I will talk about the business benefits in chapter 5.

Where did we come from? Let’s take the two Java applications from chapter 4.1 and 4.2 and put them in the context of a real system landscape, let’s say within the Aftersales department of a large discrete manufacturer. Here are typical parameters that I will use to describe what exactly has changed:

• 1 IBM mainframe which contains the whole parts’ master and various programs (Cobol)
• 15 productive custom systems implemented in 3 different programming languages (Java, C++, VisualBasic)
• 15 productive relational databases from 3 vendors (Oracle, Microsoft, OpenSource)
• 50 interfaces/adapters between systems; an integration infrastructure is in place but it is not connected to all systems and it only works in asynchronous batch mode
• 3-tier landscape (development, integration/test, production)
• 5 out of 15 systems are dedicated reporting systems (duplicated data, read-only)
• The overall data volume in these 16 systems is around 35 TB

You do not need much imagination that all of this is very complex, expensive and that the data quality in the systems is very low. As described in chapter 4.2 we have migrated step-by-step all the databases to HANA which was the easiest part. We got rid of the duplicated data and implemented a basic data model with HANA views on top of it. The client-server applications in Java, C++ and Visual Basic ran pretty much out-of-the-box. Unfortunately the customer has used some database vendor-proprietary technologies which caused some extra efforts but which we could adapt with the help of intelligent tools. That was the same with the mainframe which now runs on the Corporate Data Pool as well. The Cobol programs were translated into Java applications.

Here is the list of benefits that we realized by doing all this:

1. Shutdown of mainframe. This dramatically lowered our cost of operations from a hardware and a knowledge perspective as there were not many Cobol developers left
2. The business logic of our 15 application continues to work like before but now at high speed with accurate and consistent data
3. We shut down 45 database servers and replaced them with 3 HANA servers
4. We shut down all of our 50 interfaces and decommissioned the integration software
5. We decommissioned all 5 of our dedicated reporting systems as we do not need them anymore. We are able to do reporting on transactional data at any time in any granularity.
6. The overall data volume decreased to slightly below 4 TB. We run the whole data pool on a single scale-up machine. For further data growth we will implement the “dynamic data tiering concept” of SAP HANA as we do not need all of our historical data in main memory. This will again decrease our memory consumption by another 50-70%.
7. We decommissioned 80% of our backup capacity. More than that we are now able to make a consistent backup of all our business data and restore it to any point in time if we needed to.

The benefits for IT are huge. And all that we did was basically exchanging the database.