Is your data pulling its weight?

Quality in, quality out 

Successful use of BI and analytics starts with knowing what makes good data. You can provide the most stunning visualisation for a business but the output will only ever be as good as the data going in. 

From the perspective of the customer, good data, or rather, correct data is critical. From personalised emails that label you the wrong gender, to clumsy sales calls for services you don’t need, we’ve all been at the receiving end of misplaced marketing messages where the data held on you doesn't quite match up. If customer data is wrong then, undoubtedly, the customer experience is not going to be a good one. 

But changes are afoot, driven in part by data governance. GDPR has been a contributory factor in improving the way many businesses handle data. It now costs resource and money to store and secure customer data, so if it’s not adding value then there’s no point keeping it. 

From a business perspective, data must be linked to business objectives. Our survey has revealed 25% of CIOs simply don’t know whether benefits are being derived from BI and analytics, suggesting that some CIOs are involved in delivering data projects without fully understanding how they will be used. Similarly, 41% of CIOs stressed that having no clear business brief is a barrier to moving forward with BI and analytics projects. 

Manage what you mine

Next to quality, management is the next critical factor. I have worked with clients whose marketing teams spend as much as 80% of their time creating data sets and 20% of their time on analysing it. With all their time tied up at the front end of the machine, they have no time to analyse and action the insights they’re getting. If you can’t make your data work for you, what’s the point in mining it?

In the last two years, 90% of the world’s data has been created. Knowing what to do with it is far from straightforward. I often go into businesses that have so much data they get overwhelmed and don’t know where to start. That’s where curation comes in. 

A key challenge in maximising data is knowing which of it to use, and when. When you don’t know what you don’t know, how can you effectively explore the data you produce? Data curation is a relatively new function that, by combining the skills of data scientists and data analysts, allows businesses to determine what information is worth saving and for how long. 

It’s the difference between looking for a book on Amazon or going into a Waterstones store. The former will present you with a million options. The latter uses expert sales staff to find out what you’re looking for and guide you to a range of options. 

Businesses looking to enable self-service analytics will utilise data curation to give internal teams quicker access to the data they need to make commercial decisions. This could be in the form of an in-house data curator or support from an external team. But the outcome is what’s important.  

Make your data project pay from the start

Getting tangible benefits from BI and analytics requires resource, but it also requires ongoing investment. And where there is investment, there must be strategy. Joined-up thinking across the business and a laser focus on the return on investment can help businesses avoid common stumbling blocks when starting BI and analytics programmes for the first time. 

It’s sadly quite common for organisations to jump into data projects without ensuring the ground-work is in place, with the digital wing of the business introducing new BI and analytics process only to be tripped up by old business practises in other areas. 

There can be a tendency to overlook the ROI of BI and analytics projects, which can be a huge risk to the business. My advice is to break the project down to start driving outputs and getting results you can use quickly. I have seen many organisations stall because they embark on huge, monolithic projects that take years to complete and can become outdated during the time of implementation. This can be easily avoided by adopting agile working practices - going through cycles and iterations that produce results you can start using immediately. 

Is respect for data a cultural thing?

BI and analytics are essential for innovation; the pace of which is defined by the pace of valuable insight coming into the business. So developing a data-driven culture is a critical component. 

We need to change the way we interact with data, from the customer data we store to the intelligence we mine and use to make better decisions. With a clearer focus on business outcomes, organisations can harvest their data more effectively or even decide whether or not to invest in large BI and analytics projects. I still go into businesses that want to introduce BI and analytics without truly understanding the business challenge. If your business is running effectively then why implement it? 

So how do we build a data-driven culture? It must start from the top down. BI and analytics advancement can improve business practices and help make better decisions, but they must be part of, and driven by, strategy.

The cultural shift is already happening. Our understanding and use of data is changing as new blood enters the market. Today’s entrants have grown up with data, at home with functions like Apple Siri and Amazon Alexa, which allow us to get answers really easily. This is truly a data-driven generation. 

We’re almost at a cusp where for every CIO used to the old world, there is a new data-savvy millennial joining the industry. It's no longer just about the technology, it’s the people and processes behind the tech that are driving change and changing company culture. 

Where we see BI and analytics really pulling their weight is where CIOs aren’t afraid to experiment with their data, using agile working to go through cycles and iterations rather than attempting large scale, monolithic BI. Getting results you can use straight away to improve the business is the secret to bringing the rest of the C-suite on-board for the journey.

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