Covid-19 has forced organizations to quickly alter the way they manage data to maximize its value to survive and compete. Inertia, together with a lack of foresight, is all too familiar from previous crises. What is different this time is that the legacy companies are struggling the most. While many legacy companies are under strain, newer, digital-first businesses are rising to the challenge. The fitness phenomenon Zumba, for example, went from 100 percent face-to-face training to fully online classes in just six weeks during the initial lockdown period.
Most organizations are now asking themselves challenging questions that will change their data and analytics strategies for years to come.
What will the next disruption look like? How can you see the signals earlier and react faster? How can we effectively leverage our workforce to ensure a smooth transition to new ways of operating? How can the organization become truly data-driven?
So what data analytics trends can we expect in 2021? Here are three key trends to be aware of.
1. The mass switch to cloud and SaaS will continue
Organizations have rapidly realized an integrated data and analytics strategy is essential to seizing opportunities and making quick pivots to match market volatility that will be the new norm for years to come. For many businesses in 2020, the increased use of cloud providers and online services has been essential to keeping the lights on in virtual environments.
This has prompted companies to overcome the inertia and red tape surrounding SaaS, PaaS and other “aaS” products. While bigger projects have been put on hold in the short term, the immediate switch to SaaS will be a trigger for a greater migration of databases and applications. The pace of innovation in data and analytics is swift, and SaaS provides immediate access to new technologies like augmented analytics, facilitating transformation.
Companies that continue to resist this pivot are going to quickly see erosion in any competitive advantage they may have had previously. Relying on legacy on-premise data environments eliminates the tremendous scalability and cost-savings benefits, along with the nimbleness and real-time access to data, inherent in successful cloud and SaaS data and analytics deployments.
Containers and serverless infrastructure hold great potential for running applications in the cloud but using them at scale requires organizational maturity and significant know-how. The ability to manage hybrid deployment across multiple clouds will continue to be key to avoid vendor lock-in.
However, despite the embrace of SaaS and cloud-based models, what many organizations forget is this acceleration also comes with inherent cultural challenges. It’s essential that IT and technology specialists embrace a new way of working with data from being the gatekeepers to facilitating self-service across the entire organization.
2. Data storytelling will be consumed by the masses
In 2020, data and data visualizations exploded in mainstream news driven by Covid stories. Now more than ever, we’ve seen the importance of data storytelling. There has been a massive up-levelling in the conversation about data, where during the height of Covid you had armchair epidemiologists saying things like, “Here’s the problem with comparing per capita.”
According to IDC, by 2022, a third of G2000 companies will have formal data literacy improvement initiatives
in place to drive insights at scale, create sustainable trusted relationships, and counter misinformation. There’s already been tremendous growth in data literacy initiatives and free training, including those from The Data Literacy Project, that can improve data skills democratization. Organizations must leverage these initiatives to create a culture where everyone is able to successfully work with data.
As self-service analytics is democratized and the workforce gains more access to more data, IT and business analysts must evolve their remit to expand beyond core dashboard creation and setting up data repositories. They need to see themselves as change agents, fostering a culture in which users of all skill levels are empowered to make contributions within the analytic ecosystem. IT has the opportunity to become data stewards and enablers, helping to propose new ways of organizing and providing data, identifying process improvements, scoping business opportunities and driving operational efficiencies.
3. Business-ready data will be more critical than ever
Since the start of the pandemic, there’s been a surge in the need for real-time and up-to-date data. Disruptions to supply chains, with hospitals scrambling to procure PPE and consumers stockpiling toilet paper have highlighted this need. In the case of PPE, the reaction to the shortage was too slow; with toilet paper, consumers broke the supply chain by assuming a shortage where none existed.
As the velocity of data increases, the speed of business needs to match it. It’s now essential to have “business-ready” data available in real-time, that is, data that is not only curated for analytics consumption but which has timely business logic and context applied to it. The infrastructure and applications are available, enabling a gradual transition to active intelligence. That will be a big factor in helping enterprises pre-act. In the mid-term, triggering actions on data at the speed of business will be key to move from reactive to pre-active and AI will play a significant role in this.
According to Gartner, by the end of 2024, 75 percent of enterprises will shift from piloting to operationalizing AI, driving a 5 x increase in streaming data and analytics infrastructures.
In times of crisis, we must challenge ourselves to transcend existing ways of doing things, evolving past practices and looking outside the boundaries of convention. As much as we may initially think of Covid and the post-Covid era as a time of contraction and retrenchment, it can also be a time for redefinition and growth.
For businesses, one of the most important questions is how can we prepare for the next disruption, see the signals earlier and react faster? To tackle future anomalies, you have to move from being only reactive to being “pre-active” – that is, to both prepare and act based on real-time data. For organizations everywhere, the moment to embrace new data and analytics models is now. It’s been a turbulent year, with many lessons. And if we get this right – and act accordingly – we’ll be much better prepared for the next disruption.
Geoff Thomas, SVP APAC, Qlik