Re-Imagining the Paradigm Around Data Analytics
The relationship between business and data is constantly evolving. Organizations today are doing everything they can to harness data insights for decision-making — from upgrading analytics systems to hiring for new roles like Chief Data Officer.
But becoming a truly data-driven organization requires more than just the right tools and roles; it’s about re-imagining the paradigm around data analytics.
Here’s more on how this looks.
A Flexible Approach to Data Analysis at Scale
The latest paradigm in data analytics involves giving everyone across an organization access to everything — with the ability for administrators to set granular permissions for individuals and groups, of course. Organizations are realizing the value in empowering users to analyze data on the fly, at scale, on an ad hoc basis rather than limiting them to pre-defined data sets and dashboards.
Modern data analytics software lets users look at all the data at scale, rather than restricting them to certain slices, sources or pre-built dashboards. For instance, someone using the relational search tool from ThoughtSpot can query data whenever questions pop up over the course of a workday or whenever they need data to inform a business decision. They’re then able to keep drilling down into their findings, which come automatically formatted as interactive charts.
When you compare this process to the act of referencing a pre-built dashboard or relying on static charts for limited answers, you can see the broader shift happening. Data is becoming increasingly accessible on demand, and at scale.
Data Governance Drives Strategy & Value
Data governance has traditionally been associated with security, which is certainly an important component of figuring out how to make data analytics accessible while also protecting sensitive information.
But the very idea of governance is expanding from data management and security — like enforcing permissions — to something meant to drive business strategy and value. According to TDWI, expanded governance “should be viewed as the engine for transforming your company into a data-driven organization.”
Effective data analytics governance requires an increasingly big-picture understanding of the entire system. Leaders need to do more than simply keep an eye on who’s using data; they need to optimize how the organization is deriving business value through data. Governance increasingly means constantly evaluating how well the organization is able to drive decisions and action using data — then implementing improvements as needed.
It’s no longer enough to consider governance the domain of IT administrators. Organizations need other leaders to take an active role in tying data analytics to strategy, driving value, and forging a data-driven culture from the top down.
Artificial Intelligence (AI) Revolutionizes Decision-Making
Data democratization in search-driven analytics has given a wider range of employees throughout an organization the ability to run ad hoc queries on data — asking questions and getting insights in seconds that they can then incorporate into their decision-making processes.
Now think about all the answers to unasked questions lurking within data. If your company is relying on data analysts to manually uncover insights from within billions of rows of stored data, you could be waiting awhile. And in the meantime, your analysts are consumed with this task and unable to work on higher-order projects.
The answer? AI-driven analytics, which use algorithms to quickly uncover potentially useful patterns and relationships within data — then push these findings to human users. After all, decision makers can only incorporate the information they have into their choices; AI is capable of pushing more insights to teams they can use to affect change and drive business outcomes. The machine-learning component of AI data mining helps algorithms refine themselves over time to understand what constitutes a relevant insight.
The paradigm around data analytics is changing thanks to advances in AI analytics, data analytics software, and strategy-minded governance.
