The rapid growth in AI and machine learning means that data is a core business asset. Leadership is critical – and by bringing data into the boardroom, companies can ensure they are keeping pace or even leading in a fast-moving and increasingly digital world.
Five steps to bringing data into the boardroom
The rapid growth in AI and machine learning means that data is a core business asset. Leadership is critical – and by bringing data into the boardroom, companies can ensure they are keeping pace or even leading in a fast-moving and increasingly digital world.
There are five steps to consider, says Lewis Gillhespy, Executive Advisor at Rockflow Resources.
This article is based on a talk Lewis gave at Bentley Systems, London, for the Scottish Energy Forum.
How often have you heard leaders state we need to treat data as an asset?
In today’s world, every company is a data company, but many hold onto industrial age thinking and just don’t give it the priority it deserves. Whether your firm is a startup, an SMEs or a multinational, data – and how it is managed – will differentiate how your company succeeds. The advent of machine learning and AI means this trend is projected to continue accelerating at an incredible pac.
When handled well, the benefits are significant. Recent research by the Harvard Business Review and Google Cloud found the organisations that took a data-driven approach fared best over the past few years. Operational efficiency, revenue, customer loyalty and retention, safety, and employee satisfaction all increased among data and AI leaders, when compared to other respondents. However, if we’re honest with ourselves, we rarely treat data with the same care as our other business assets.
Mature SMEs may have terabytes of raw data, dozens of different platforms, metrics and KPIs. There may be sites worldwide, each with singular approaches and measurements, resulting in a “hairball architecture” – a complex, tangled system of IT applications with multiple bespoke solutions with varying amounts and quality of data flowing in between.
Too often, this results in siloed thinking which may drive particular approaches in one function or geography, without any regard to the impact on the end-to-end business as a whole, or indeed the potential all of this data can bring.
Indeed, one survey by Deloitte found while 49 percent of businesses are using data analytics to drive process and cost efficiency, only 16 percent are using it to drive strategy and change. If businesses are to fully harness the power of data, they need a roadmap to digital transformation which should include these five steps:
- Lead The Change
Leadership is vital. Whilst every company will have a CEO, COO and CFO, very few have a Chief Data Officer. But this is starting to change.
Forward thinking firms are now appointing CDOs or VPs of data analytics, underlining the strategic importance of data to company growth – and recognising that traditional C-suite appointees may not have the skills to drive change in the digital data world. In reality, companies need a blend, with key personnel that are digitally savvy but still understand the core business.
Incredible new software tools such as digital twins can deliver data-rich dashboards that track pan-organisation activities in real time – equipping leaders with the information they need to deliver improved efficiency.
So, by elevating ‘IT’ and placing data in the boardroom, leadership teams can start to harness the benefits of the digital age, by simplifying systems to allow for rapid improvement of data quality.
This will involve:
- Standardising how work is done: to reduce variations
- Simplified systems: reducing the number of data sources and improving their quality
- Controlled data management: implementing systems that are easier to control, measure, and improve
- Culture change: training people to a new way of working and how best to treat data as an asset
- Develop A Business Case
In most firms, a business case is usually required to justify kicking off a project. However, data driven initiatives often have many interlinked parts, and unless these are clearly articulated, leaders question the value.
Again, leadership is vital. Often projected benefits are substantial, but don’t land in the department which creates the data. If for instance, the executive committee knows that better data controls will result in a 20 percent improvement in overall staff productivity – those leaders will ensure that all departments get on board, whether they stand to benefit directly or not.
- Tackle The Root Cause
When data problems rear their head, we often create “data heroes” of the people who spend their nights and weekends trying to correct bad data or find missing data. But far better than having data heroes fly in, is to tackle the root cause issues – and to do that, we need to invest in root cause analysis to logically inspect where and why “bad” data is being generated.
As a data customer, leaders should be intolerant of accepting bad data and should provide clear feedback to those who created the data so that they can improve the data for the next business cycle. Constructive feedback and good collaboration allow the data to be delivered in the right format, the right way, every time.
- Measure What Matters
As the adage goes, ‘garbage in, garbage out’, and AI tools will only ever be as good as the data you feed it. By the same token, too much data can lead to paralysis. Leading firms need to invest in data literacy and develop simple metrics which inform business strategy and contribute to where the company wishes to go – which I describe as ‘the critical few versus the trivial many’.
Not all data is equal, so you want to look for your value drivers. Where will the desired quality of data make the most difference, and how will this be measured? For instance, in oil and gas, the focus is often centred around seismic data. But seismic data has limited influence over the rest of the value chain.
It’s only used by geophysicists. And that’s not to say they’re not important, but when you compare seismic to well data, which will be vital for drilling, geophysicists, and finance, it helps define what data objects provide the most value.
Most of the value for the company is going to be in understanding forecasts, costs and inventory – so make that a focus.
- Keep Learning
In any business, lessons are learned over time, and all good companies look to capture learning so the next business cycle can be improved upon. But have you ever performed a look back on your data quality? Have you ever tried to learn and improve so you’re ready for the next data type? And if not, why not?
Leaders primarily have two responsibilities: first to run the business and second to improve how the business is run – and data quality is vital to both. Getting high quality data the first time avoids unnecessary rework as well as more reliable business insights. It’s tempting to look at new technologies to help modernise and unlock new ways of working. But ultimately the technology will be of limited use if its inputs are not correct. The other side of the coin also holds true – once you have systems generating high quality data, you’ll be able to plug in AI and machine learning technologies and find real traction.
Leadership is critical – and by bringing data into the boardroom, companies can ensure they are keeping pace or even leading in a fast-moving and increasingly digital world.