The rapid evolution of data ecosystems that’s transpired over the last decade demands a transformation in how data is managed, analyzed, and utilized for decision-making.
To fully understand how the world of data analytics is being reinvented today, we have to go back to a not-so-distant past when many of us were working “on-prem” and with infrastructure silos. This was a time when data insights were locked away behind dashboards that only a select few “certified experts” could access. Dashboards were the data product, security was hard to manage, and nothing was integrated. Business intelligence teams tried to help find deeper meaning in our data, a.k.a. “insights”, but instead they were bogged down in building and updating dashboards and reports, or more often, extracting data for users to manipulate in spreadsheets.
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As a result, BI was confined to aggregated, historical data trends. Exploration was either constrained to a drill path someone created (trying to anticipate your question despite not working in your job), or left to aggregated, downloaded data.
CEOs today have an unprecedented opportunity, and indeed an imperative, to rethink their approach to data practices and business intelligence driven by the widespread enterprise-level adoption of cloud databases like Snowflake and Databricks. We can no longer remain content to make pivotal decisions based on summarized, stale data. Today, companies must act in real time, which requires live data, accessed “raw” and by the decision maker to ensure peak accuracy and velocity. And with the pace of data generation accelerating, and the increasing availability of third party data, there are are a myriad of new ways to make data not just a part of business operations, but also part of product offerings as well. Operating with old data, where fundamental decisions are based on inaccuracies, is worse than running on no data. For too long, the business intelligence industry has idled, entrapping us in a cycle of mediocrity.
Here’s my advice on where leaders who are ready for change can start and what considerations are the most important to keep in mind.
Imperative 1: The Need to Build for Scale and Security of Data
It is a false trade-off that companies must choose between governance-and-security and data access. The struggle lies with traditional BI tools’ inability to scale and to enable end user customization, resulting in compromised security through data extracts to end users. These “end user applications”, effectively customized data views, changes and workflows built on top of spreadsheets, are the most common execution path for departmental users. Only solutions engineered to handle massive datasets scale seamlessly alongside business growth without compromising data security as data remains secured and governed in the warehouse. This capability is crucial as enterprises increasingly prioritize cybersecurity, aiming to maintain data integrity and lineage without sacrificing accessibility and speed.
Imperative 2: Democratizing Data Through Integration and Accessibility
Modern enterprises are characterized by their diverse data needs and the varied technical skills of their employees. A unified platform that supports multiple data access points, from SQL queries to natural language processing, ensures that all employees, regardless of their technical proficiency, can engage with enterprise data effectively. By eliminating the need for multiple BI tools, leveraging collective expertise and facilitating real-time collaboration, decision-making across departments is greatly enhanced.
The only way a company wins is if everyone can act on real-time, raw data. The old way meant technology limiting data to experts. That has to end. No more infrastructure silos. No more outdated dashboards and spreadsheets. Let’s make decisions based on facts. Data needs to be for everyone.
For competitive enterprises to work synchronously and at high velocity, you need to provide your employees with multimodal user interfaces; ranging from natural language and spreadsheets to SQL and Python, to build and communicate together. Their expertise is their job function, and technology barriers should not be created to impede them. Technology should amplify people’s existing skills and enable them to collaborate. A world where data science is natively integrated, for example, with spreadsheets means that the benefits of the emerging AI and ML worlds can be easily adopted by the spreadsheet communities in your lines of business. And to further fuel speed and accuracy, tools that enable secure communication: live edit, annotation, chat, comment and more, ensure that employees across the globe can work synchronously.
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Imperative 3: Ensure Adaptability and Innovation
The pace of technological change requires data tools that are not only robust but also future proof. As we’ve seen with AI and machine learning algorithms, companies need to adopt the latest technologies to stay ahead of trends and automate decision processes. This adaptability is particularly valuable for enterprises looking to innovate continuously and maintain a competitive edge in their industries.
What makes rapid adoption possible is being mindful of the -ilities: Scalabilty, reliability, auditability, and so forth. Platforms that abstract users from underlying tech (i.e. an LLM, a data science platform) but provide access to their outputs (model outputs) ensure that users benefit from new technology with overheads in change management or the need to work across systems.
And this is just the beginning. With the technology available today, CEOs and business teams can use business applications to more effectively track a marketing campaign, manage stock inventory, assess employee performance, and report crucial compliance data. We just don’t need “super analysts” anymore. In fact, we are rapidly moving from data trends (BI) to data apps (historical trends → forecast conditions → automation) built by end users. This creates all of the benefits of the aforementioned end user application without the security risk and limits to scale.
And if business performance were not motivation enough to take advantage of these new architectures, regulation such as the the Data Accountability and Trust Act demand action at the risk of fines and worse. CEOs have a responsibility to lead this charge and invest in cutting-edge data platforms that empower employees of all skill levels to collaborate seamlessly, break down barriers, and encourage a culture of data sharing and exploration.
As enterprises continue to navigate increasing complexity, the adoption of forward-thinking BI tools will define the leaders in data-driven business practices. It’s hard to get rid of legacy systems that are entrenched in enterprises but recall an important motto to lead by as you take these important next steps: we build for the future, not the past.
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