What makes data actionable

Bilde av Anja Loug Helland
by Anja Loug Helland

25. Jan 2021, 6 minutes reading time

What makes data actionable

Many organizations today struggle with getting access to data. We would argue that not only access to data, but transforming that data into actionable insights is one of the three key challenges an organization must solve to become data-driven. In this article we will cut through the noise and summarize some important points to get you started.

The point of having actionable data, not just data, is of course to make better decisions – and hence move faster towards achieving the goals the organization has set out to solve. A typical goal could be to create additional business value through increased revenue, or to make a positive impact on the world. As such, even a charitable organization could make use of data-driven decisions to optimize utilization of its resources.

Ultimately, we consider this a never-ending step-by-step process from needs → information gaps → preparing and working with data → effect measuring, education and feedback → new or altered needs. So let us take you through what makes data actionable – and how to spark this feedback loop to supercharge your data-driven organization.

A starting place
Actionability of data, again, is really about whether or not it can support your business decisions. This is why you need a top down approach.

If you start by simply diving into the pool of insight, you might spend the next 5 years splashing around not really getting anywhere. But if you start with a very specific business need, KPI or goal, you have a sense of direction – and know what data to look for.

A simple recipe to extract value, then, is to ask:

  • Which of our business goals can possibly be supported by data?
  • What can we actually measure – and what is the data quality?
  • What actions are we able to take based on the data we collect?
A specific business goal can be everything from a retail store wanting to get a heads up when X amount of customers enter the store, a realtor discovering potential sales ahead of competitors – or a production facility seeking to reduce errors and loss.

Quality data – actionable data
Raw data can be incomplete, difficult to interpret or simply too detailed for a business user. Generally, this means it is rarely actionable. To reveal its great potential, you need to model and preferably combine data from different sources. This enables you to see possibilities you otherwise would not – and to make decisions with greater certainty.

This requires a high degree of data quality. To avoid the ever-looming ghost of GIGO, any data-driven organization needs to be able to truly trust its data. Therefore, you need to be familiar with the process of mapping out the quality of data streaming from all the faucets in your organization. If you want to learn more about the 6 dimensions of data quality, check out our article about data as a strategic asset.

As the volume of our data is growing in both size and complexity, any organization aiming to act upon it needs to provide the proper context to its data. These contexts should be framed with a specific business goal in mind.

Just enough, just in time
Keep in mind that for data to be actionable, it also needs to be accessible for the business user. As granularity is always more detailed in specialized systems, you might need to combine data sources and include business logic to cater to the end user’s needs. With the right technology, however, you can create agile data pipelines that ensure much needed adaptability in an ever-changing world.

Accessibility for the end user can also mean not flooding them with more data than is necessary to support that specific goal. If a manager is simply interested in knowing how many employees are absent on a given day, that data should be very simple to extract. If the manager, however, wants to use data to support their goal of reducing employee absence numbers below a certain threshold, more granularity is needed (on the other hand, there is no data urgency).

This brings us back to the mapping of data quality aspects, which should assist you in knowing what needs can be supported by which data. And it also brings us back to culture, where a positive feedback loop can emerge from the back-and-forth between data users and internal data facilitators. The end-result is what any data-driven organization should be working towards:

A summary – and a recipe
The key takeaway here is that actionable data is data that enables a decision process. This entails keeping a top-down view, where data utilization is anchored to a specific business goal or need.
  • Step 1: Understand your business decisions – from goals to specific processes
    • What decisions do we need to take?
    • What information do we need to support each decision?
  • Step 2: Understand your data gaps
    • Compare existing data with information needs and identify any gaps.
    • Fill the gaps with data quality improvements (make everyone in your organization aware of the value of quality data input), combine data across internal systems and add external data.
  • Step 3: Provide data fit for purpose to a specific decision and train users (a good tip is to make data fun – employ gamification elements if possible and suitable for your organization’s data maturity)
  • Step 4: Get user feedback, monitor business effects and reiterate back to step 1 and 2.

If you do this, while making use of smart automation tools to support an agile approach, you should be well on your way to not only have access to data, but to act on it.

Giving the organization access to relevant i.e. actionable data, is one of the three pillars described in the e-book of how to build a data-driven organization.

Download free e-book: Building a data-driven organization

Anja Loug Helland

Anja Loug Helland

Anja is Head of Advisory and co-founder of BI Builders and has worked with data warehousing and data platforms for over a decade. Having experience from a range of different clients, Anja is an expert in understanding our customers' needs and map that to data and data models to get business value.

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