Industry executives and experts share their predictions for 2021. Read them in this 13th annual VMblog.com series exclusive.
How Data-Driven Businesses Can Adapt to a Volatile World
By Dan Sommer, Senior Director, Global Market Intelligence Lead at Qlik
While COVID-19 continues to be the most important factor of 2020, businesses and governments are already starting to think about what happens if a global pandemic strikes again. As our world becomes more interconnected, it’s clear that the frequency and impact of major market-disrupting events will increase. And while no one can predict when a transformative event will happen next, certain preparations can help organizations manage data and thrive on anomalies in a way that their competition can’t. When looking towards 2021, we’ve identified five immediate pivots that will affect the way businesses collect, process, and analyze data moving forward. If we get these right – and act accordingly – we’ll be much better prepared for the next big disruption. Here are the five trends that we believe are important when thinking about data, analytics, and digital transformation in the new year.
SaaS is everyone’s new best friend
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. Organizations that once claimed they would never look to SaaS have suddenly embraced it. These changes have had some beneficial side effects, like adding scale and elasticity. Additionally, the pace of innovation in data and analytics is swift, and SaaS provides immediate access to new technologies like augmented analytics, facilitating transformation.
So 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. Technologies that can access, move and harmonize data from multiple places will follow. 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.
Evolving from self-service to self-sufficiency
In a virtual world, self-service needs to evolve. When there are no instruction manuals and no one there to hold a user’s hand, a fast, intuitive ramp-up becomes a hygiene factor for adoption, and compelling user interfaces will no longer be a nice-to-have. But we’ve also seen that users often don’t want to self-serve; they increasingly expect insights to come to them. As a result, we’ll see more micro-insights and stories for the augmented consumer. In addition, data is too often overlooked. Empowering users to access data, insights and business logic earlier and more intuitively will enable the move from visualization self-service to data self-sufficiency.
AI will play a major role here, surfacing micro-insights and helping us move from scripted and people-oriented processes to more automated, low-code and no code data preparation and analytics. If more people can be self-sufficient with data earlier in the value chain, anomalies can be detected earlier and problems solved sooner.
Up-to-date and business-ready data are more important than ever
Since the pandemic arrived, we’ve seen a surge in the need for real-time and up-to-date data. What is usually fairly stale – quarterly business forecasts, for example – is fleeting and mutable now. Alerts, data refreshes and forecasts will need to occur more often, with the freshest variables. On a macro level, we’ve seen disruptions to supply chains, with hospitals scrambling to procure PPE and consumers stockpiling toilet paper. In the case of PPE, we reacted to an actual shortage too slowly; with toilet paper, consumers broke the supply chain by assuming a shortage where none existed. Surges like these are accentuated in a crisis, and we have to build preparedness for them.
As the velocity of data increases, the speed of business needs to follow. Can we make “business-ready” data – data that is not only curated for analytics consumption but which has timely business logic and context applied to it – accessible earlier? And can we automatically trigger the end points, whether that’s an automated process or an action taken by a human? The infrastructure and applications are available, enabling a gradual transition to active intelligence. That will be a big factor in helping enterprises pre-act.
It’s essential to capture and synthesize “alternative” data
How early could we have detected COVID-19? Studies of “alternative” data – in this case, traffic data outside hospitals in Wuhan and keyword searches by Internet users in that area – indicate that the virus may have been circulating in late 2019. The investment community has been a pioneer in using alternative data, including audio, aerial photos, water quality, and sentiment.10 This is the front line for data-driven innovation, and getting an edge here can result in huge gains. But in the wake of 2020, alternative data will become mainstream, with the goal of spotting anomalies much earlier.
From that, we can get derivative data, which comes from combinations, associations and syntheses with data from systems of record. As IDC says: “As more data gets captured and becomes available from external sources, the ability to use more of it becomes a differentiating factor. That includes taking lessons from industries other than your own.” 11 This trend, similar to what Gartner calls “X analytics,” 12 isn’t new but is finally becoming an important foundation of modern data and analytics, thanks to cheaper processing and more mature AI techniques – including knowledge graphs, data fabrics, natural language processing (NLP), explainable AI and analytics on all types of content. This trend is completely dependent on ML and AI, as the human eye can’t catch it all.
Collaboration has to coalesce earlier in the chain
In 2020, we’ve seen a step change in the embracing of web conferencing, remote collaboration and online learning. We’re in a new world where we can’t gather as often for a quick huddle in the office or sketch out an idea on a whiteboard. Also, an increasingly fast-moving world means that in many cases, people don’t have time to wait to make a decision while someone builds a dashboard. The convergence of data management and analytics in the market has created opportunities for integration points between the components of a data pipeline, combining synthesis with analysis and enabling active metadata, business logic and catalogues to act as connective tissue. This in turn will push collaboration, innovation and discussions to the data itself.
We’ll see more experiences that introduce easy and enjoyable ways of working together in areas that were previously rendered “boring” or “difficult.” Tasks for moving data from raw to analytics-ready will become more engaging, fast and iterative. The separate, siloed worlds of data curators and consumers will begin coming together, and business logic will persist, enabling analytics-ready data to become business-ready much faster.
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About the Author
Dan Sommer is a Senior Director and global lead for Qlik’s Market Intelligence Program responsible for the supply, demand, macro and micro picture. He also focuses on executive and strategic content, like data literacy, data-driven culture and trends in the industry. With more than 18 years’ experience in the IT industry, Dan is a frequent keynote speaker, blogger, and columnist. Dan has previously held the positions of Research Director, Agenda Manager and Global Lead for BI and Analytics Markets at IT research and advisory company Gartner. During the ten years he spent as an analyst at Gartner, he specialized in markets, trends, competitive landscape evaluations and go-to-market strategies.
