In scrambling to put together their data maturity ‘puzzle’, many businesses are losing sight of the bigger picture. While data and analytics are key components, they are nothing without the understanding, technology and talent to put these pieces together.
However, the final picture in the data maturity jigsaw is always changing and businesses must acknowledge this to meet their goals. Regardless of which industry a company occupies, maintaining progress toward insight-driven decision-making requires ongoing evaluation and constant review. Here is what businesses need to consider:
The missing puzzle pieces
It would be impossible to complete a puzzle without first seeing what is missing. Businesses early on in their journeys might need to address a lack of quality data sources, or improve their interdepartmental coordination. For organizations further along the path, they may need to develop a more robust infrastructure to support their analytical tools.
As these missing pieces are unique to each company’s operations, circumstances and commercial objectives, there is no one-size-fits-all formula for reaching data mastery. Through pinpointing the challenges it faces, a business can build a strategic course of action to resolve them efficiently. Approaches could include making a single source of truth accessible to relevant teams and stakeholders or streamlining how data is organized. Evaluating challenges and potential solutions should be a continuous process, so organizations can consistently move closer to insight-driven decision making.
Change begins with opening conversations around areas marked for improvement. Businesses often assume introducing new analytical tools is the be-all and end-all of advancing their data journeys, but transformation needs to start from the inside.
The false narrative of investing in world-class tech
According to forecasts, over the next five years investment in tech solutions is expected to make up the lion’s share of the big data and business analytics market. Fuelling this is the belief that data maturity relies on having the latest, greatest technology. Research shows, however, that half of the senior marketing professionals find themselves disappointed with the results of their tech investments. Clearly, there is a disconnect.
Instead of blindly allocating resources to new solutions, which typically come with a large price tag, businesses should concentrate on optimizing their existing tools and infrastructure. Technology is without a doubt integral to data analytics, but it is not a quick fix for realizing data maturity. The groundwork should be laid first – establishing what a business wants to gain from analytics, where it is in relation to its objectives, and what it needs to achieve them. This must take priority over onboarding new solutions and form the corner pieces of the puzzle.
Putting people first
More than 37 percent of employers believe their teams lack the advanced digital skills necessary to their organizations, while 60 percent anticipate their need for these skills to rise in the next five years. Evidently, cultivating the right knowledge is of critical importance. By starting with its people, a company can ensure continued progress toward its insight-driven aspirations. Whether providing relevant training, recruiting new talent, or redefining established processes, a business can begin transforming its approach to data analytics. Once this is in place, it can then return to the question of ‘what problems can technology solve’, but it is crucial to be discerning when implementing new tools.
Embracing a new breed of artificially intelligent analytics
Agility ranks as a priority for 87 percent of companies and the technology they onboard must support this. For instance, businesses that adopt a customer-centric focus will need to keep up with ever-changing consumer demands. Companies that sit within especially dynamic sectors, meanwhile, need to be prepared for rapid innovation and developments. To make the most of any tech investment, organizations should look to solutions that deliver adaptability alongside ongoing value.
Predictive solutions, such as analytics tools powered by artificial intelligence, enable businesses to be proactive in their decision-making processes. Following a year of unprecedented change, analyzing historical data points to gain a view of what has happened is no longer enough to give organizations an edge. Companies are now trying to look forward, be able to anticipate what is coming, and use actionable insights to inform their next steps.
AI analytics with predictive capabilities reveals trends, patterns and anomalies in real-time, so businesses can remain agile no matter the environment. When coupled with data expertise and supported by an insight-driven culture, predictive analytics becomes a valuable part of companies’ journey to data maturity. For example, a B2C business can quickly identify emerging consumer trends and adjust marketing spend to tap into new opportunities, generating higher revenue. A B2B business, on the other hand, could use predictive solutions to anticipate the impact of particular events – such as price fluctuations – on its supply chain and performance, then prepare accordingly to minimize disruption. Adopting tech that drives value for multiple operations is an effective way to ensure results from investment.
When transforming its approach to data analytics, a business needs to start with small steps. By cultivating a progressive attitude, reflecting on current challenges, and setting up the people and practices to facilitate best use of available data, companies can begin piecing together their data maturity puzzle. These actions will frame the big picture, ensuring organizations take an informed stance on managing budget, talent, and tech solutions. Incremental change will guide an organization along its unique path to data maturity, helping to realize their goal for insight-driven decision making.
Alexander Igelsböck, Co-Founder and CEO, Adverity