Have you started on your data journey but struggling to break through the hype and fit the disparate parts of the data puzzle together to deliver the digital success you hoped for? The Oakland Group’s Richard Corderoy delves into the real world of big data.
Are companies getting better at putting their data houses in order?
This is a difficult question to answer because few organisations have an understanding of what ‘good’ should look like. There is still a general lack of business knowledge of how difficult, time-consuming and what is actually involved in delivering successful data science projects, which is why so many have tried and failed. This may explain why the average tenure for a CDO is around 2½ years signifying how difficult it is to make the necessary changes and achieve tangible business results.
Our experience shows very few organisations have their data house in order, it is a huge task to bring hundreds of different systems together. We see a huge imagination gap between what companies want to achieve and what they are prepared to invest in. However, it is not all doom and gloom, for the businesses who have invested heavily in their core systems they are reaping the benefits.
However, there is no getting away from it, this is really complicated stuff. Add to this a proliferation of systems, tools and technology, management focus and the managements knowledge of how to do this work and we start to enter a murky world and this is before adding the company zealots for whom perfection can be the enemy of good.
The data industry is very noisy, confusing, loaded with jargon and unless you are a full-time data professional it is almost impossible to digest and keep on top of. In addition, many organisations have tried something and it either hasn’t delivered the anticipated results, largely because the data engineering was much more difficult than first anticipated or the team of talent wasn’t quite right.
What are the business implications for companies with poor data management?
Poor decision making, material risks, GDPR and commercial compliance are just the headlines. It is worth understanding what are the business risks, the causes of these risks and how to prevent them. Move beyond the hypothetical and onto the real business issues.
Operational management, how do you know if you can trust the data you use to drive your business and how many businesses are still driven by a spreadsheet? There is a huge richness in the data that can be unlocked which can drive businesses forward giving them the competitive advantage, this data can be used to inform how to make the business better, provide new customer services or improve efficiency. Lots of this potential can be lost as the data is just too difficult to actually get at.
Many businesses have the desire to do more and know the right answers but the answer is to start with something small, driven by real-world issues that you can solve vertically top to bottom using data extraction, data cleansing, data analytics, and data implementation. Realise the benefits and move on. But it is important to be realistic, if the data is poor quality then you won’t be able to make the progess you hoped for.
How can companies get their data in order?
Start managing data as a thing, in the same way, your businesses understands its core processes, understand how the data supports those core processes. Data and process are opposite sides of the same coin. For example in a manufacturing plant, you could ask anyone to close their eyes and walk through the production line and they would be able to tell you physically how things work, but very very few can do the same with the data journey. By knowing how data and process combine can help you to break down organisational silo’s. It is surprisingly rare for process and data owners to effectively work together and it is often complex. Who owns the customer data – is it marketing, is it sales, is it production or service?
It might sound obvious but keep it simple, know what systems you have and what data is in them. We have found that only a few actually know what systems they have and how they are utilised.
Don’t let zealots bury this under paperwork and too much governance.
Don’t try and do everything at once, again learn the lessons of process and quality, volumes of processes did little to improve many businesses.
What best practices should a good data culture encourage?
The first question to ask is why are you striving for a data culture? You should be aiming for a great customer or service culture, data can help you do this.
What you can do is identify things in the way your business works that either encourage poor data behaviours, for example, does a line manager ever check the sales force is imputing data correctly? On the flip side are good behaviours such as rewarding the leadership team who engage and want to know where data comes from, when it was taken and what systems it was taken from? Both of these things will help you make better decisions and deliver for your customers.
How can your company embrace these activities as best practice?
It is hard work and a real change and businesses need to understand these issues can’t be solved for free or by buying yet another piece of software. One thing to mention is there is nothing new here, businesses have been facing these same challenges for many years, whether that be with quality or safety.
Our advice is always to understand that digital transformation doesn’t happen overnight and requires the literacy and ability to understand the role of data and analytics across the business and ensure you align your data strategy with your business strategy.
Find the right partners to work with and achieve results with smaller projects so you can prove the value of the work which in turn creates advocates who will support and drive through what needs to be done.
Oakland is a full-stack analytics, process and governance company. We don’t believe in silver bullets, but we do passionately believe the opportunity is at the intersection of these three things.
