Companies that aren’t continuously reinventing their business – with data at the core – will end up watching from the sidelines while their market is disrupted. Data technologies, science, and processes are rewriting the rules of business and propelling organizations toward digital transformation.
You probably know that already, right?
But what you might be underestimating is the incredible, frenetic pace of digital transformation. We’re witnessing a radical rethinking of how an enterprise can use technology to meet customer expectations and dramatically improve business performance.
At the foundation of this blink-and-you’ve-fallen-behind speed of change is the intelligent management of data throughout the enterprise. Too often, enterprises embark on an Artificial Intelligence (AI) and Machine Learning (ML) initiative only to belatedly realize in mid-development that they’re missing key performance indicator (KPI) data that they did not foresee needing when they started. They may also not have the right people trained for the required work – or enough of them. Such mid-process realizations can stall or even cripple digital transformation initiatives. Simply put, AI/ML doesn’t function without the right data – right at the start.
Or your best-of-intention efforts will be doomed to failure.
Companies clearly see data as an important asset and understand the pressing need to adjust their strategies to emphasize data and analytics. In a recent NewVantage Partners’ 2019 Big Data and AI Executive Survey, 92 percent of executive respondents said they are increasing the pace of investment in big data and AI. But conversely, the survey also found that the percentage of firms identifying themselves as being data-driven has declined in each of the last three years – from 37.1 percent in 2017 to 32.4 percent in 2018 to 31 percent this year.
Clearly, leaders feel like they’re losing ground.
Although the catalyst for digital transformation can vary from one organization to the next, there are five common drivers for data strategy that can help reverse this “falling further behind” trend and help ensure success for any business.
- Unification of business and IT perspectives. Creating a common data strategy ensures that the business and IT departments are positioned as joint leaders of the company’s direction. There’s complete alignment in understanding each other’s needs, capabilities, and priorities. This way, companies can adopt a “business-led/technology-enabled” approach not only for internal operations but also for vendor and partner collaborations.
- Launch a reskilling initiative. Forward-thinking organizations design well-planned and ambitious retraining strategies. Telecommunications giant AT&T initiated a massive project after discovering that “nearly half of its 250,000 employees lacked the necessary skills needed to keep the company competitive.” This much we all know: the skills shortage will only intensify as technology capabilities continue to evolve. HR departments must develop a solid strategy that accounts for the type of skills the workforce will need in the future for their organizations to stay competitive. We have to accept that “skills” more than “jobs” is the new currency of business. While organizations can’t protect every job that inevitably is going to be made redundant and/or obsolete by technology advances, they can protect the workforce by reskilling and upskilling as many employees as possible. It’s not just doing the right thing for your people. It’s doing the right thing for the business.
- Enterprise-wide alignment of vision and guidance around leveraging data as an asset. Getting everyone on the same page with a comprehensive data strategy ensures that different groups throughout the enterprise view data-related capabilities in a consistent manner. A comprehensive data strategy enables a single “source of truth” when it comes to reliable customer, vendor, and product master data across systems. Clear rules on “systems of record” reduces redundancy and confusion. It creates repeatability – a key outcome of consistency. Finally, it reduces operational costs, optimizes performance and drives better decision making.
- Define objectives and key results (OKR) criteria across the enterprise. The data strategy should define your success and quality. This will reinforce consistency for how initiatives are measured, evaluated, and tracked across all levels of the business. Setting expectations is critical.
- Reduction of technology debt. We all want to get the most out of our technology investments. But at some point, maintaining our legacy implementations becomes “technology debt.” They provide limited business value in relation to cost, performance or quality needs. They require constant time and resources for upkeep. They also hinder the adoption of more innovative technology or business practices that can drive the business forward. These barriers to advancements are both costly and complex to alter. That’s why it’s crucial to have a data strategy that takes the current state of the enterprise data environments and operations into account while providing guidance for applying innovation with minimal disruption to ongoing business operations. You should consider moving to a state-of-the-art data platform and analytics tools. The short-term cost of time, money and resources will be worth it in the long run with better insights and overall health of the business.
There are game-changing opportunities on the horizon. But any enterprise that seeks to enable automation and leverage AI must first develop a robust data strategy that prioritizes quality and contextualizes that data.
Make no mistake: strong, actionable data is the difference between having AI/ML engines work smarter or making your IT staff struggle without the right skills in hand. As leaders, we cannot just leave our employees to fend for themselves during this transition.
Creating or updating your data strategy and reskilling employees to meet the agility of technological change is the only way forward.
Listen to my conversation with Nick Mehta, CEO of Gainsight, as we discuss what it means for companies to be truly data-driven and customer-centric.
