Decision-Making at the Speed of Business Means Better Data Onboarding
The pressing need for real-time data pushed businesses to accelerate and expand their investments in data and data management solutions during 2020. While much of the industry focus is rightfully on new advancements in predictive analytics that are driving business value, it is becoming increasingly clear that architecture and data teams are working equally hard to enable this data with more modern data architecture and new data sets. Recent research from IDC, commissioned by Qlik, revealed that in Q1 2020 alone, 30% of organizations were conducting major architectural changes and 45% were adding new data to their analytics environment, significantly expanding the capabilities in their analytics arsenals. Multiple cloud platforms, on-prem sources and even offline documentation, such as direct mail surveys, are all feeding inputs into the modern data pipeline.
All this new data isn’t translating into value or impact all of the time. Data engineers and data analysts are struggling to unite all of these data sources in a strong insight-driven pipeline. Many spend up to 80% of their time simply trying to identify the data and assess it, leaving little time to turn data into insights that drive the business. Creating valuable analytics will require us to flip that ratio on its head. Organizations need to be able to mine information at the speed that the competitive marketplace requires to succeed. Effectively converting data into active intelligence – a state of continuous intelligence from real-time, up-to-date information designed to trigger immediate actions – requires businesses to reconsider how they onboard their data in a way that simultaneously sets it free for analytics use and protects the enterprise from risk. Here are tested strategies on how to evolve data onboarding to fuel real-time analytics and decision-making.
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Every day there are critical business questions to be answered – how many employees do we have and where are the employees? What is our forecasted profit this quarter? Where are our least productive business units? But not all of these questions are top of mind for the C-suite. And a good place to start an analytics journey is there, creating analytics that drive the most important strategic objectives of the business. Data onboarding should align equally well to these objectives. In short, strategic objectives need analytics and analytics need data.
Collaboration among this stakeholder group, specifically when it comes to data onboarding, creates the crucial organizational alignment needed to deliver high-quality data at the speed of innovation. By exploring data onboarding strategies, teams at different levels and with different skills are able to build a 360 picture of the data, how it’s captured, stored interrogated and leveraged. That understanding will shape smarter and more efficient strategies that can have a strong effect on reducing processing costs through reduced time in developing and capturing insights.
One aspect of the core DataOps frameworks you develop will be aligning data storage strategies with usage and value of data. In the first generation of Business Intelligence, complex transformations loaded data to data warehouses, which created precise but brittle data transformations. This was good for answering the same question over and over but not great for answering slightly similar ones. The pendulum then swung dramatically to data lakes, which made raw data available to everyone, but left it up to the developer to make sense of what the data meant and how to use it. At the same time, traditional infrastructure that supports core production systems are dependent on batch uploads and extended cycles, and struggles with managing the data volumes, speed and readiness required for real-time data usage.
