A short history of Data
Tons of data are produced at different enterprise levels every day. Many different ways exist to classify data. There are operational data as well as analytical data, structured and unstructured data, internal and external data, actual and metadata.
On operational level one can find transactions, transactional states, transaction Metadata, reconciliation data, master data, pricing and calculation data, data about operational risks and customer classification. Last but not least there are a lot of business partner communications over different channels in several unstructured formats.
On analytical level many enterprises have created data warehouses and data marts during the past 20 years. The first and foremost target of those early analytical data stores was to support a comprehensive management information system by creating flexible reporting facilities.
Data Mining came into the play a few years later. Techniques such as automatic generation of decision trees with entropy measures like GINI index, clustering algorithms, found their ways into data analysis in the early days of data mining.
One big issue with data ware houses has always been the data quality and data enrichment. This is where data cleansing methods were introduced. External data sources have been used to enrich data or to get higher quality. But this was not the only intention of adding external data sources into the system. Enrichment can be used to get more data related to a subject and to correlate them with the existing data in order to achieve new insights.
The Rise of Big Data
As technology evolved and new methods of handling and analyzing any kind of data appeared the value of data as an economic property increased. Big data, data science and artificial intelligence brought new opportunities to create Knowledge from Data. To relate different kind of data to each other in ways they haven’t been related before came into the spotlight.
With those new capabilities new type of data became more interesting for analytics purposes. Data about data so called Metadata which one can imagine of as the data definition or data access statistics on the one hand side and unstructured external data such as content of social media sources like facebook, twitter, etc. on the other side.
Many topics like data extraction, transformation and loading, data cleansing and enrichment, data protection and data privacy one will face on technical level in big data initiatives have been in place in data warehouse implementation already. Others like handling streaming architectures, automatic data type recognition emerged as specific topics to Big Data.
The organizational challenges are much bigger in big data since big data has a more open scope than the very specific scope of Data warehouses.
Data Strategy, what’s that?
Data become strategic and hence there is a need for a systematic approach both on business as well as on technical level to improve value created by data.
On business level an enterprise need to define
- what should be achieved with data (vison),
- which data are needed for that goal,
- which methods are they going to apply to create new insight from that data and
- what income that knowledge can lead to?
This is what a data driven business model is about. It stands at the very beginning of a data strategy an enterprise has to develop and follow in terms of monetarizing data.
A data driven business model can be thought of the same way one creates a common business model. Imagine a
business canvas. There you have a rectangle for key resources a business needs to create a value proposition. Data are such key resources. Hence, a data driven business model must give answers to the same questions asked in a business canvas.
Coming from a business canvas approach it is also necessary to define a set of key activities to be taken in order to create the value proposition. What is an enterprise going to do with the data? Which kind of analysis methods should be applied in order to create information which leads to that knowledge? By finishing this task the most important part of a data strategy will be already delivered.
Causation vs. Effectuation
When it comes to choosing the right analysis methods for the data it is important to know the kind of problem that exists. In decision making there are two different classes of problems.
In the first one there is a given predictable effect with a known probability. The target is to choose information gathering and analysis methods to select between the means to create that effect. This is called causation.
In the second there are several unpredictable effects in place. The target is to find out which effect is more likely to emerge with given means. In this case the analysis has to apply experimental techniques to discover the underlying distribution of the unpredictable effects for given means.
In data analysis one might follow a hybrid approach where both analysis methods can be applied.
Different Data Strategy approaches from here
During getting deeper into this subject I have discovered different definitions of a data driven business model. Some experts require a data strategy should also define a project plan which describes how an actual subset of the data should be analyzed with milestones, budget and all that project management stuff we all know.
Other experts of data strategy development stop with creating a data driven business model.
One
interesting approach I have recognized starts with definition of key actors and key data. Then it switches to the customer and creates a customer profile. Value proposition is created by a so called Data-Need-Fit. From value proposition other parts of the business model such as key activities are derived.
But ….
Experts always call out the need for a systematic approach when a new idea and method appears. I have seen a lot of them in the past. Well prepared and best sold systematic approaches to master new challenges.
At the end of the day one can measure the actual value contributed to the enterprises success by the impact an initiative has to the enterprises revenue regardless of how systematic and well prepared that initiative was.
Unfortunately many of those approaches end up helping their inventors to create value.
If you want to have an advice, choose the people for implementing your data strategy wisely.
That’s the most important thing.
For further reading …
Data and Analytics – Data-Driven Business Models: A Blueprint for Innovation
The new hero of big data and analytics: The Chief Data Officer
Effectuation and Causation: The Effect of “Entrepreneurial Experience” and “Market Uncertainty”
This is exactly what people in that discussion pointed to. Existing platforms such as Amazon are for so many reasons in a far better position than banks in Germany in particular. And I absolutely agree with this. Banks need to open up their processes and services an integrate them into these existing platforms as one of the comments explains. Another questions which I am asking myself is what’s so new about this?
Back to the future …
I remember around 15 years ago there was already the call for banks to transform from a value chain to a value network. In Germany there have been a lot of papers under the name “Industrialization of Banking” about opening up product, sales and settlement of banking into three decoupled and separated main processes each of which can easily be recombined in value networks. In that concept one bank could develop products, the other one could offer them to the market and a third one could offer settlement of those products in a loosely coupled manner. That was the time when first banking fabrics for payment or securities processing came up.
If one takes the concept of value networks seriously than there is only one small step to an open banking idea where a bank can integrate its services into an existing platform such as amazon.
Why hasn’t that happened yet then? Is there some unfinished homework to be done?
If you can’t beat them join them
Indeed it could be too late for banks to become a platform themselves but it is not too late to integrate with existing platforms. If you can’t beat them join them.
On the other hand, as we know, existing platforms are becoming more and more powerful and network economics tend to create monopolies by their nature. This is being observed by competition authorities too. I hope they will not stand still and make soon sure that competition is upheld in this field. 
October was heading to its end. We decided to meet for lunch once again. We agreed on a restaurant close to the river. You could sit outside at that place. It was a warm day when we met. One of those days at the autumn’s beginning where the sun still keeps alive hustle and bustle out there.
She was already waiting when I arrived. She sat at a table outside with a nice view over the river. There was a big fat camera on the table, close to that a book about big data and analytics.
“Hey!” I said, “What’s up? Sorry for being late!” I continued. “Hey!” she replied, “Don’t worry, all good!” she smiled. “What’s that for?” I pointed to the camera. ”Oh, Photography is one of my hobbies! It’s a good light today!” she explained, sounding like she’s bit proud of her camera.
s and the amazing number of different open source initiatives for different purposes. That was what she has always been. A very skilled and open-minded computer scientist with no fear of change. She was going to start that job with a good general skillset in computer science and mathematics and no actual idea of big data. But with an open mindset and the will to learn. To learn fast.
This happened two and a half years ago.
Today she is her manager’s right-hand woman having a deep understanding of tools, technologies, what actually practically can be done and which pains one can expect in big data projects.
If you want to perform like a startup you must create comparable conditions and environments. And one of the most important parameters , possible the most important one is to operate without legacy.
And legacy in today’s startup and digitalisation context usually means:
IT Legacy.
That said my tip is to replace legacy as radically as possible. Get rid not only of the one or other core system, but possibly rid off most if not all IT systems.
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Few days ago I talked with a friend of mine with which I used to work for a long time. We used to develop web applications with 
Having a look at the strategic answers banks and insurers find around these questions one very quickly may perceive that pretty much all of them start modernizing their IT. They do this because the hope to achieve several goals at once. They all hope to increase efficiency and reduce costs. They either have missed to replace IT in the past twenty years and hence carry a lot of technical debts or even if they have done that once during that period they need to redo because of the speed of technological progress.
On the other hand they hope to create a set of new business areas where they might produce new sources of revenue. New IT may allow banks and insurers to expose parts of their business to the public so other companies might mash them up with other services and create new value propositions which in return will help banks and insurers to benefit from those kinds of network economics. Such kind of network effect for instance might appear by taking new FinTechs and InsurTechs into account. Those startups create new value propositions mostly for SME and retails business and they have a significant demand for foundation services they usually cannot provide themselves in first place.
Working together with startups in almost all cases means bringing up new services for SME and retail and this fits to the strategy. ING for instance mainly targets SME for creating new business. ING is going to spend up to 800 million Euros for continued digital transformation until 2021 implementing new lending platforms for SME and consumers.
Other banks and insurers are doing similiar things.
Indeed there are bunch of initiatives like ING’s. Banks and insurers are introducing new core systems. They will be exposing their services semipublicly soon. They will operate clouds where third party applications can run. We will see a bunch of announcements for new application marketplaces operated by insurers and banks where developers offer new cloud based apps using the exposed services and cloud operating models. They might also use existing marketplaces such as SAP’s HANA or Salesforce’s AppExchange.
I am very curious which one of those projects will succeed and which ones of those succeeded initiatives will actually survive.
The principles of network economics teach us the winner takes it all. Hence, not all of them can win.
And what I actually am asking myself is what if a developer decides to use services exposed by different banks and insurers in one single application simultaneously? Where does she publish the application? Does he publish and offer it on the marketplace of Bank A or Insurer B? On whose cloud will that application run when each bank or insurer offers its own cloud? How can hybric clouds or multi clouds establish solutions which really work? 
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(Strategy as a diagram – Credits to 