Data-driven organizations: Why the right governance is crucial

In our first post on data-driven organizations we explored why this business model is so crucial for survival, why the shift to digital service models makes this more true than ever, and what elements anyone starting out on their data-driven journeys should explore.

In this edition we’ll take a closer look at the fast-paced speed of data-strategies, how to make head or tail of the volume of data to hand and how to keep your business decisions in check.

The speed of data strategies

We hope you’ve started planning your data strategy following our last post. However, whether you’re just starting out or you’re already a member of a data-driven club, it's crucial to remember that your plans should never be static. Some of the clear benefits we explored in our last post centered on data-driven organizations having the agility required to respond to market trends, new technology and customer preferences to gain a significant financial and service driven edge.

However, if your data strategy doesn’t reflect that same level of agility you hope to achieve, those benefits will quickly begin to slip away. Factors such as the adoption of new tech trends, legal compliance, and business needs are key to check on at regular intervals and verify whether plans still hold true.

Here’s where Artificial Intelligence (AI) and Machine Learning (ML) can really give an edge. Whilst it can be normal in other business applications to produce a feasible 1, 2 or even 5 year strategy, the pace at which data and technology changes means the best approach for data-driven organizations is to adopt evolutionary architecture. Such an approach accepts that there’s no way to predict every eventuality, but that change is inevitable. AI and ML helps with this by looking at overall marketplaces, analyzing R&D efforts and their outcomes, and uncovering patterns within business data to inform both business and strategic intelligence.

Tandem Bank recently launched its cloud-powered analytics machine, Ada. The tool will provide customers with an ongoing analysis of their financial data to help optimize customer experience and help Tandem to remain responsive to the changing consumer needs.

With AI & ML in your data strategy you not only get the most from what’s going on in the present, but become hyper aware of where changes to your strategy are needed to get the most from the future – a hallmark of being data-driven.

The importance of data governance

The larger the role that AI & ML plays in your data strategy and organizational decisions, the more important data governance becomes. Two important trends to be aware of on this matter are:

Explainable AI: This centers on the importance of being able to understand and justify the business decisions made based on AI models. Many organizations face the black box problem, when the results derived from ML aren’t transparent or lead to bias slipping into business decisions. Explainable AI helps to ensure your company and executives can hold this valuable tool to account, trace how any AI-based decisions were made and ensure findings can be understood by humans.

Ethical AI: Ethical AI, or responsible AI, is a trend which champions responsible development, deployment and operation of AI as a tool. Is there a data-set which shouldn’t be fed into your algorithm due to privacy laws? Then make sure your AI model doesn’t have access. Privacy and transparency policies are crucial nowadays, but these don't have to negate your use of AI as an intelligence tool if planned correctly.

However, this raises the question of which expert should be in charge of managing decisions around your AI tools. Here, business acumen and technological expertise should come together to help define which areas of business operations are ripe for AI models and ensure they are adopted with a sensible framework.

The human eye on data

Although data-driven organizations lean heavily on the power of technology, having the right experts in place can make or break the success of the project. For this, you must appoint a responsible person to take charge of the management of data, whether that's a Chief Technology Officer, Chief Data Officer or Senior Analyst. This expertise can be sourced internally or externally, but clearly defined roles and responsibilities are a must.

The role holder will help to select the correct data architecture, avoid data silos, implement a sensible governance policy and ensure the right variables are analysed. Getting the right person in post will seriously influence the outcomes of your data-driven efforts so take time to recruit someone who not only understands the technology, but the goals and drivers of the organization. This person should also pose skills of a “data strategist”, due to the undeniable link between data strategy and business strategy on today’s playing field.

If you’re still not sure where to start with your efforts to become data-driven, speak to an intive expert today and discover the insights that lie in wait.

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