Just as most of us prefer harmonic chords to dissonance in music, value creation is maximized only by harmonizing data strategy, data organization and data governance. We will address the most important issues and provide successful examples.
Attainment of 15 – 20% growth in revenue, just by coordinating data strategy? Though this alone probably does not lead to the mentioned growth, it would be simply impossible without harmonization of data strategy, organization and governance. Data combined with analytics and correctly applied in a company's value creation processes can achieve this effect however; more and more companies are recognizing and also wanting to realize this potential.
Here we routinely encounter the question: "Which type of organization is best suited for this?" Of course, organization and its composition can be discussed, and usually there are at least as many opinions as involved executives. In our experience, no form of organization per se is the best. Organizational structure can rather be designed in different ways – each having different advantages and disadvantages. But how does one proceed to identify the organizational form suitable for the challenge being faced? It is advisable to first define the goals and type of added value to be achieved through organization. Far-sighted strategy and necessary fields of action can be formulated subsequently. Only then should organizational structure be considered and consequently serve as a basis for defining processes of data governance.
Isolated ideas and measures can appear nice, but do not achieve sustainable added value. We accordingly recommend consistent coordination of the four areas described next.
In a first step, it is necessary to precisely define goals. Arising here are questions such as "where and how should value be added?" or "which category of use cases should be prioritized?". When it comes to optimizing existent processes in marketing and sales, for example, it is often quite possible to double the conversion rate of campaigns. On the other hand, if costs are to be reduced by automating decisions in processing, 90% of previous decisions can frequently be automated and routine times consequently reduced. Should new data-based products ultimately be created? This is the supreme discipline and I am happy to contribute my experience here.
Data vision is a source of motivation for long-term goals and should be formulated across use cases: "Marketing activities are optimized with data." Of course, this vision must also be tailored to the specific initial situation. Once this has been done, it is also important to formulate fields of action in order to realize the related measures. It is important to ask oneself: What does this mean for organization? For processes? For infrastructure? And last but not least, for staff competence? Decisive roles are played here by professional business understanding, IT equipment, statistical flair and – very importantly – social competence. Orchestrating these diverse requirements together in a meaningful way is always a challenge.
Now let's consider the subject of organization / reorganization. Analytics & data organization are to be understood as all aspects of the structure of BI organization, DWH and technical-analytics teams, as well as AI special teams. Which competencies interacted less than optimally in the past and should be restructured? Which ones have to be rebuilt from the ground up? Where can we expect cost reductions through synergies? Where do we need more critical mass to make decisive progress? Schemes here accordingly range from central teams through bi-modal models to three-stage organization. If challenges lie in professional understanding, then organization should probably be departmentally oriented in order to focus this understanding. If challenges lie in technical competence, then organization should probably be oriented according to technical components in order to quickly build up the requisite equipment. Obviously, the focus of challenges can also shift over time.
At least as important as organizational structure is process organization within and across departmental boundaries. How do different teams cooperate to address data quality, manage metadata and coordinate work without redundancy? To achieve a short time-to-market for new data products, governance can allow duplicate operations to a certain extent. However, operations which negate each other must definitely be avoided. Logically, the aim here is to provide support with standards and guidelines in order to properly understand data assets and use them easily whilst complying with data protection.
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Managing Director
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