Varun Babbar is Managing Director for Qlik India. Varun is responsible for spearheading Qlik’s India operations in the region empowering customers in India & South Asia to lead their businesses with data by enabling them to see more deeply into customer behaviour, discover new revenue streams, and balance risk and reward. Varun comes from a strong technical background, having started his career at Qlik almost a decade ago where he joined as the Director of Presales for India. Prior to joining Qlik, he spent more than a decade in various leadership roles managing clients in Switzerland, USA & India at both IBM and Infosys.
In the new market dynamics, no business will succeed by doing it alone, so the strength lies in sharing resources to innovate and build resilience.
Today, our biggest societal issues are systemic, such as the pandemic, climate change, and economic inequity. In this environment, digital transformation is not enough: one needs digital innovation.
External forces have a much bigger impact than they used to, and supply chains have been fundamentally disrupted. Digital giants like Google and Amazon have built massive networks of data collection which they are using to dominate, replacing entire markets with a few powerful supply chains that cut across industries. At the same time, digital startups are disrupting from underneath, though they struggle to reach scale.
To take a leadership position, business must build a collaborative value chain that can resist being disrupted. Here are data trends that change competition as we know it in 2022.
Collaboration-mining arrives: The massive shift to work-from-home has made it imperative to quickly embed BI within workstreams and through productivity apps like Teams, Slack, and Zoom. Within these apps, opportunities opened for more collaboration with outside stakeholders. Just as we have learned to mine data and processes, we will see the advent of “collaboration mining,” deriving data from workstreams on how decisions are reached and actioned. This will enable decisions to be tracked, providing crucial auditability and boosting trust with multiple stakeholders.
The dashboard is dead. Long live the dashboard: Displaying KPIs and visualizing data is something everyone can do. It is true that monitoring a cockpit of static visualizations does not differentiate in today’s market.
Instead, the dashboard is evolving into an analytic hub that catalogues insights and distributed data – a place where machine, process, and collaborative intelligence can co-exist. This change will interweave information producers and consumers, uncovering discoveries that inform direction.
Distributed clouds become the norm: The data landscape will continue to be messy and hybrid for the foreseeable future. According to 451 Research, most organizations no longer look for a single, all-encompassing solution to their IT needs but rather an IT estate that accommodates the cost, performance, and governance requirements of different workloads. A distributed cloud infrastructure strengthens your ability to both access and share interwoven data securely and confidently.
Data lineage provides explainable BI: For years, analytics users have struggled to explain the data behind a metric, KPI, or calculation. Data comes from both inside and outside an organization, from multiple source and inputs. We will never have a single version of truth because data changes in nanoseconds, and because there are constantly new variables that need to be accounted for.
In an intertwined world with multiple versions of the truth, data lineage, with augmented metadata management, will be mission-critical to triangulate data, providing trust and “explainability”. When users have visibility into where the data comes from and where in the lifecycle it is, they gain the confidence and trust to act on the insights the data drives.
Insight velocity brings cost into focus: As cloud data warehouses and lakes have been broadly adopted, they have opened the opportunity to live-query huge amounts of data directly. But when businesses use this technique, they can end up with runaway cloud compute costs.
On the data integration side, businesses should be able to choose between continuously updating and merging data (incurring higher compute costs) and doing an aggregate view (with lower costs). And from an analytics perspective, they should be able to choose between live-query (higher compute costs) and in-memory exploration, which can be both faster and cheaper. Figuring out their unique needs, organizations will have to figure out how to run the right queries in the right places.
Data Science overlapping with analytics upskills everyone: In a world where data is widely available and business users can create their own applications, data literacy is a critical foundation. Augmented by AI and low-code, today’s technology is enabling everyone to get involved without needing programming skills.
Data science has long been seen as something only the highly-skilled few can do. But if common predictive use cases – like key driver analysis, whatif scenarios, and on-demand predictions via APIs – become more accessible for regular analytics consumers, they will enable more people to do more. A scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.
Conclusion: Things are changing fast.
In today’s business landscape, longstanding boundaries are blurring. A competitor can become a partner, a partner can become a customer, and a customer can become a competitor. The solution is not to wall off but to lean into a new form of competitive edge: generative relationships with mutually beneficial outcomes. The only option is to become more “interwoven,” creating a trusted ecosystem built on clear rules of engagement. This will generate joint data, insights, and innovation that wouldn’t be possible independently.
As markets are increasingly dominated by a few strong value chains, one can not do it alone. Open platforms – with APIs – introduce unprecedented opportunities for you to forge partnerships that create interwoven value chains. The data and insights generated will become joint currency, giving you and your partners the resilience to thrive.