Leveraging Your Data Science Team

How to effectively leverage data and analytics to power your business

Aug 19, 2020 · 5 min read

This is the second article in a 2-part series!!

Value creation vs value extraction

In a previous article we discussed the value created by having an effective data science team in place to ingest, sort, analyse and model company data in order to generate all sorts of interesting insights that can (and should) influence decisions made at all levels across the entire organisation.

The business value created by such insights ranges from identifying specific new trends and opportunities for market expansion to simply creating a more data-informed workforce.

However, a common — and oft unidentified — issue for most businesses is these generated insights not being applied in decision-making by the relevant business agents. Why is this, you ask? If insights are being uncovered but are not communicated effectively to the right audiences around the organisation, then your business is not leveraging the power of its own data correctly. The very act of data science becomes pointless.

We’ve all heard of the philosophical thought experiment “if a tree falls in a forest and no one is around to hear it, does it make a sound?” that raises questions of observation and perception. A similar idea applies here:

If your data team conducts analyses & discovers new insights, but nobody ever learns from it, did the data science even happen? Or worse — does it matter if it happened or not?

How to truly leverage your data team

So in this way, the value of data and data science is directly linked to how it used and applied. It can either have no impact on business performance, it may affect only a small percentage of a company’s results, or it can spark long-lasting business growth.

Today all companies are data & insight rich, so why is it so hard to extract this value? There are, of course, a few bottlenecks that need to be identified:

1. Allow business agents to drive data projects

Data scientists and engineers may very well be the masters of data. But it is the business agents around the business that are the domain experts in the various departments — whether that be marketing, sales, finance, infrastructure, etc. As such, it is these individuals — the non-technical stakeholders — who should be ensuring that new projects are focused on business outcomes.

In this way, the data team is not controlling the narrative, but continues to influence decisions made around the business by driving analytics-based actions at all levels of operation.

2. Make knowledge curation automatic

Data scientists want to run analyses, discover insights & effect a positive business impact on the company. But the issue is that the tools they use are very technical, and converting results to more readable formats — copy & pasting graphs into slack and email, sharing PDFs, etc — for a wider audience remains a persistent hurdle.

But they shouldn’t be managing the curation process themselves anyway. This needs to be automatic —if the goal of collecting data, modelling, and making predictions is to help everyone to contribute to organisational business objectives, the link between knowledge creation to knowledge sharing needs to be as seamless as possible. It will allows both sides of the aisle — the data team and the various decision makers — to be more effective in their functions. The journey from computation to publication of results needs to be integrated into a single workflow.

3. Promote discovery over sharing

There is a huge difference between sharing and discovery — in a previous article, we discussed this difference and how in order to fully benefit from generated data insights, you need a more comprehensive, unified knowledge management system for all your data-based reports.

We share results to turn insights into action. But if sharing is only happening upon request instead of on a one-to-many based system, business agents across the wider organisation will be less effective in their respective roles. Why? Because the data insights relevant to their positions are not being communicated effectively to them.

So keep your analytics knowledge in one place so the entire team can learn from your business data & apply these insights to daily decision-making.

4. Build a 2–way communication flow

In most organisations, regardless of the platform they are using, data science involves a one-way communication flow — the data team pushing content to everyone else. But as discussed in point one above, it is the business agents that are the domain experts.

Unlock the potential of your data & analytics by allowing these employees to discuss results, ask questions and provide feedback to your data team. The value here is that the data-science team can react to what the rest of the company needs and also gives them the ability to measure the impact of new experiments.

This leverage in action

In the first part of this series we discussed the value a data team can bring to the business, across 7 different areas (there are many more, as you might imagine). Now that you have the correct systems in place, your business is truly prepared to start taking advantage of this value.

  • Business agents are empowered around the business because they now have access to a unified data journal, where they discover insights week-to-week, some of which will be relevant to their roles.
  • From these analyses and data insights, new opportunities are identified, and these can be discussed and acted on across the organisation.
  • By providing a medium through which your data scientists are connected to the C-level executive team & other high-level stakeholders, your overall business objectives & long-term goals are data & metric driven.
  • Now that the rest of the team can ask questions & request follow-up projects and analyses, even non-technical audiences are driving experimentation and idea creation in their respective domains.
  • In the same vein, these workflows will also aid in identifying cost-saving solutions across the business at much faster rate than would otherwise have been possible.
  • Your business becomes more data-driven. Everyone can now learn from data and contribute to greater business value by making more data-informed business decisions.
  • For most businesses, it is the management level and above that define overall strategy on how to gain competitive advantage in the market. With a more knowledgable workforce, you effectively have a larger brain than competitors to sense new market opportunities and threats, the individual nodes of which can take authority over the roles and seize these opportunities.

Final thoughts

Today, most organisations around the world are amassing unbelievably large amounts of data with the objective of deriving value from it. The problem is that many are, in fact, gleaning very little value from their analytics. This is not due to a lack of insights discovered, but rather the absence of these insights in business decision-making. These companies are rich in data and data insights but are knowledge-poor.

By focusing organisational data science on business outcomes, allowing business decision-makers drive projects, and bridging the gap that divides data teams and all other stakeholders, companies can begin to truly turn data science into tangible and measurable business results.

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