Everybody Else Is Doing It, So Why Can't We? (Data Strategy: Part 2)

Simplicity & Evidence

An effective analytics and data strategy will drive business growth and profitability.

In our recent blog post “Everybody Else Is Doing It, So Why Can’t We? (Part 1)”, we outlined four key steps to building effective Data Strategies. This article deals with the first two; demystification, and ensuring robust and relevant case studies that demonstrate real business benefits. Analytics has often been described as a black box, so unless we address these two points, how can really expect CFOs to sign off on investments?

Demystification

Simplifying complexity is the first step. We must clearly explain what we actually mean by analytics and data.

It is not all about massive and complex datasets being processed in the cloud, using the very latest machine learning or AI. Analytics covers everything from basic reporting upwards. It covers all types of data, whether from traditional market surveys, or large customer and social media databases.

So let’s start by breaking it down into broad categories:

What is striking, is that the questions being asked are the same questions we’ve always asked. As a career market researcher, who qualified with a Statistics degree over 25 years ago, what’s also very encouraging is that much of what constitutes the analytics world is the same as it was many years ago. The essential theories and methodologies are very similar to those that have been in use for decades. What has changed is the processing power and storage capabilities of modern computing technology. This has facilitated the growth and collection of data and the development of software tools and system architecture to process these data.

Returning to the point of simplifying complexity, language is our most powerful tool. On the flip side, language is also the tool which most aids obfuscation. As an example, Artificial Intelligence and Machine Learning are two particular phrases that may conjure up terrifying images from Isaac Asimov or Gene Roddenberry (feel free to choose your own personal cultural reference point). While these are clearly advanced and developing tools, we should bear in mind what they are actually doing at the simplest level. We must ensure we then explain this clearly in putting together our plans.

For both phrases, it’s important to note that we have not entered into the world of the conscious machine (yet!), although at face-value the language used might suggest so. I believe this is something that can put some people off, so it’s important that we are clear in our explanations.

Case Studies

As well as explaining our objectives and plans simply, we need to ensure we can provide relevant use cases. The must demonstrate clear business success and be relevant to the client and client’s sector.

For example, for a TV broadcaster this could be showing how a simple report into audience demographics aided targeting of sponsorship activity (i.e. Descriptive Analytics). Or, it could be demonstrating how a sophisticated model accurately predicted viewing levels for a new drama serial (i.e. Predictive Analytics).

As an indication of the broad reach of different analytics techniques, the results below from 500 data analytics professionals demonstrate that analytics is not just about increasing sales. It also covers process efficiency, drives business strategy and aids product refinement. So it’s important to consider all benefits to the business and not just those which might interested the CMO. Again this can help get cut-through to the CEO and CFO in terms of developing a top-down data driven culture.

Major ways organisations are using data and analytics worldwide as of 2018

Summary

So as part of the journey to build a data strategy we need to make sure that we clearly understand and explain what needs to be done, providing relevant case studies that demonstrate real business impact.

The next article will focus on step 3 of the process; the challenges in terms of internal politics, resourcing, infrastructure and relevance. We’ll then explain how everything comes together to pull an effective strategy together.

[1]. Forbes, Microstrategy: 2018 Global State of Enterprise Analytics