We at Norwegian Cognitive Center is now starting to learn more and more about the different organizations out there and we experience a broad difference between the various companies challenges due to their data maturity – so it is time for us to set more focus on this topic.

According to a survey of more than 300 executives by Bain and company, a two-thirds of companies surveyed stated that they were investing heavily in becoming data driven and 40 percent of the respondents expected significantly positive outcomes. Despite high expectations, 30 percent of the same executives had not a clear strategy for embedding data and analytics in their companies. So how to get there

Data is the core of AI
If data is the lifeblood of the digital enterprise, Artificial Intelligence (AI) technology is its pumping heart. AI, especially its subsets including machine learning, deep learning, and advanced analytics, can automate much of the insight gathering and decision making in a data-driven enterprise, and amplify the value of data many times over.

The good news is that it is straightforward (and does not require huge investments in tech and lots of new tech hires) to use data to grow your team’s capabilities and deliver more value to your employees. The first step is to recognize where you stand today in your journey to AI maturity.

Data Maturity
Big Data Maturity Model (BDMM) is a qualitative method to show the growth and increasing impact of big data capabilities in an IT environment from both business and technology perspectives. It comprises a set of criteria, parameters and factors that can be used to describe and measure the effectiveness of the big data adoption and implementation.

Primitive: initial stage of disconnected activities in an unorganized fashion, also called manual data processing. Since every individual is creating a unique document, this manual approach lacks consistency and results in large amounts of time wasted digging into how specific numbers were reached and verifying accuracy, rather than understanding what the data is trying to tell us.
Tentative: ad-hoc experiments of trial and error with some level of organized data management - Death by dashboards as some will call it. Both manual data processing and magical dashboards mask one of the core problems at this stage of maturity: multiple, inconsistent sources of truth. Large amounts of time are wasted in creating and navigating these tools in an attempt to reach answers
Advanced: comprehensive framework and lifecycle for effective execution where data tells a story and give you tactical value. This typically starts when efforts focus around understanding what information employees in various roles need to do their jobs successfully. Focusing at the employee role level also uncovers that answering the most valuable questions (what is our performance — and why?) requires viewing and combining data from multiple systems currently being used across the organization. To prevent this from leading to yet more information overload for the team, aggregated data must be stripped down to what is specifically required to answer the questions at hand. This simplified, role-focused approach allows the data to be turned into glanceable answers for employees, giving them everything they need on a single screen to understand what is going on and make better decisions accordingly.
Dynamic: consistent operationalization by means of reference architecture and best-practice patterns meaning that intelligence emerges. Organizations start to see real business results, the speed to produce value starts to increase as employees learn the tools, improve their understanding of the data, and grow more comfortable and receive proactive information. The corporate adoption stage is characterized by the involvement of end-users, an organization gains further insight and the way of conducting business is transformed. During this stage end-users might have started operationalizing big data analytics or changing their decision making processes and most organizations would already have repeatedly addressed certain gaps in their infrastructure, data management, governance and analytics. Improved business outcomes should be more tangible and measurable as your data maturity progresses – and your business model is now depending on big data.
Optimized: converged platform with a repeatable process and policy-driven codification and have a transformed organization. NB: Only a few organizations can be considered as visionary in terms of big data and big data analytics and operate at this level – but we are aiming to reach this goal. At this stage, Machine Learning is mature, your data is clean, and your teams have broad data science skills. Your employees use data to work on behalf of each other and customers. They are less bound by restrictive rules and approval hierarchies and more empowered to use data to ‘do the right thing’. In general, the organization is operating with a flatter structure and governance is more cross functional than department led. The organization can execute big data programs as a well-oiled machine with highly mature infrastructure. They have a well-established big data program and big data governance strategies and executes its big data program as a budgeted and planned initiative from an organization-wide perspective, The employees share a level of excitement and energy around big data and big data analytics.

So where on this stage is your organization?

Different sources:

Measure Your Organization’s Data Maturity

Are you a startup with ai in focus?

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