I have been working in Data and Analytics for 17 years and have seen many different ways to manage data and analytics across a business. Working in mature data driven businesses you realise that the legacy of having a lot of data in a business can be as much of a hinderance as a help. 

Invariably data, and the teams that use it, have grown organically within departments and therefore tend to work in silos on short term projects, reacting to immediate departmental needs.  They often manage their own data and software, with individual support from IT. Moving from this situation to more effective ways of working can be painful.

In my experience working with much smaller or less mature businesses gives a great opportunity to get things right from the start. Here are some of the key points that, during my work as a both an analyst and a consultant, I have come across and in order to be effectively data driven, businesses need to consider.

Business vision

When a truly data-driven retailer I worked with recently was struggling, it was my view that it was too reliant on data. People in the business were endlessly looking to the data to tell them whether they should move more upmarket or focus on value. Data can inform this decision but not decisively. Businesses need to have a clear vison and strategy to ensure their data works for them.

Senior oversight

There is great benefit in data and analytics being overseen by a governance team of business leaders representing both users and consumers of the data. This team can set the data and analytics vision and strategy, aligned to that of the wider business and oversee cross functional project prioritisation, usually moving the focus to long-term strategic projects. This team is best placed to effectively agree a program of technology and data developments with IT and procure appropriate business wide software.

Structure

Agreeing an effective structure for your analysts to work in is key. This structure will depend on the size of your business and the number of functions using data. There are advantages to having a centralised team, working on business wide strategic projects, using the same technology and sharing skills and experience. I believe this can sometimes impact on the business knowledge of analysts and their understanding of the issues faced by your internal customers, so a balance needs to be struck between the two structures.

Project prioritisation

Time and resources are always tight in any business and the conflicting priorities of your analysts, your customers and business leaders can be difficult to balance. The governance team play a key role in getting this right and can help to ensure data objectives support business objectives, that there is a mix of short term reactive and long term strategic projects and a clear process for measuring model effectiveness.

Becoming data driven

Too often, businesses jump straight in to big investments in technology or people without understanding their requirements and having someone with extensive data experience involved is key to making things run smoothly in the future. I believe that it is necessary to get the basics right to enable data driven decision making.

Reporting

Often simple performance reporting is time consuming for analysts to run, not prompt enough to make effective time sensitive decisions and lacks sufficient insight to support decision makers. The procurement of new technology and software can be transformative but the financial benefit needs to be investigated to ensure the investment is worthwhile.

Segmentation

Enriching data and creating useful segmentations can add vital insight to performance reporting. Segmentations can be based on numerous data types and attributes including customer, store, product or region. Once useful segments are established based on business need, long term performance trends can be more clearly identified and support better investment decisions.

Well-designed segments can be rolled out across business functions to support diverse areas such as Brand, PR, Marketing, Property, Buying, Price and Promotions and Operations.

Store catchments

Understanding your store catchments, and the customer types who live and work in them, is vital in the current competitive environment and there are a number of ways to do this using both internal and external data. Each method has it’s own advantages and disadvantages and experience in this area is useful rather than buying generic data from the company with the best sales pitch.

Multichannel

Every successful retailer needs a multichannel strategy and strong customer propositions including cross channel marketing, click and collect, store returns for online purchases and stock information. Data is at the heart of creating a seamless customer experience and this is where data teams must work across departments in order to deliver the experience that today’s customers expect.

Modelling

Predictive modelling at business, store, category, customer and product level can be an effective tool to take data based decision making to the next level. It can be implemented on an ad-hoc basis, to quickly improve current initiatives, in particular marketing campaigns. The long term goal should be to automate both model recommendations and results but that involves significant investment in both technology and people. Both breadth and depth of experience is required to understand when and where predictive modelling will be most effective and ensure the right conditions exist for successful implementation.

Red Tiger Consulting can offer practical, cost effective advice on your data strategy. Through interviews and workshops, we determine the analytics maturity of your business and then work with you to create a road map of data and analytics initiatives to help you improve data utilisation.  So if your business needs expert advice to ensure they are making the most of their data contact Anne@redtigerconsulting.co.uk or Steve@redtigerconsulting.co.uk to find out more. 

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