Time, resources and degree of commitment required for the digital leap are often underestimated in organisations.
Many companies and other organisations possess strong competencies in operations-related analytics and production management. Yet, when they are aiming for new business benefits or creating new types of business, there is a large question mark hovering above the data strategy leading to the goals.
Many organisations have management approval for experiments in artificial intelligence and data. Yet experiments are doomed to remain nothing more than experiments unless there is serious, long-term commitment.
There must be business-related grounds for transforming into a data-driven organisation, and the transformation should always start from business needs. In addition to a strong business focus, experience shows that success requires not only top management approval, but also that top management assumes the role of a change leader.
Organising data governance is key. In a large or complex organisation or ecosystem, the amount of work needed on data governance grows in multiples. It is typical to underestimate how much work is needed to collect and maintain data while overestimating the quality of the data in possession of the organisation at the start.
Competencies in the necessary technologies must also be high enough to realistically validate the best technology or technologies to be deployed in the organisation. At the start, it is often a sensible decision to embark on the journey together with an external partner and first conduct an initial analysis to evaluate the organisation’s competencies and cultural preparedness for data-driven operations.
Competencies in data exploitation are defined as data maturity. Traditional information systems, ERPs and reporting, however efficient, are only the entry level (level 0), as far as data-driven operations is concerned.
Level 1 endeavours to find proofs of concept for data-drivenness and the exploitation of artificial intelligence. Data processing moves from reporting to analytics. Management is committed to the change and demands a predictive operating model. Commitment also means the willingness to invest in the change instead of mere experiments.
Level 2 has a data governance model in place, data lakes/storages are up, analytics built up and process automation using artificial intelligence has been initiated. Top management is leading the change. New type of operations can be started.
Level 3 sees all operating processes automated and people’s efforts redirected from routine tasks to handling exceptions. Artificial intelligence is in full use. Significant competitive advantage is being realised.
Level 4 has all ecosystem partners connected with each other through interfaces. In an ideal situation, the organisation has created an ecosystem and interfaces that become a de facto standard all actors in the industry want to join.
Most organisations are presently on level 1.
Benefits start to appear only on level 2 as described above. Getting there requires intensive work for two–three years, combined with sufficient resources – in addition to management commitment and leadership. When the organisational strategy, management and business focus are changing, the question “how can the organisation endure in the long term?” is often the culmination point.
Good examples of organisations that have reached level 2 can be found right here in Finland. The Helsinki and Uusimaa Hospital District (HUS) is an example of a level-2 actor, having created a foundation for full utilisation of data and artificial intelligence. Two years of sustained development work is beginning to bear fruit. Presently, the organisation is building an ecosystem around health data, which has already attracted large international actors wishing to do research work in the HUS environment.
It is difficult to start data-driven operations. The benefits seem vague, which makes decision-making difficult – do we need this, is now the right moment, what are the competitors doing, should we wait for technology to develop further? Additional obstacles come in the form of commitment, time, resources and relatively high investment needs.
Tieto has excellent competencies and references in working as an analytics and transformation partner for developing data-driven business. Usually, the starting point is an organisational maturity analysis, in which 50–100 people are interviewed to clarify the organisation’s mindset, realistic competencies and preparedness. Focused experiments are used to find suitable use cases. An interactive development model is used to find a formalised and scalable method to create a workable data strategy and transformation path.
Many organisations are pondering what to do. Do you know where your organisation is on the scale of data-driven competencies?
Fredrik is a leading expert on data-driven business-transformation, with a passion for finding new business opportunities from ecosystem-based co-innovation and co-creation. He is participating in significant national and international programs and ecosystems innovating and bringing the data economy to the next level.
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