While businesses rely on a variety of data storage and analytics solutions, data lakes promise to pave the way toward scalable, flexible, and resilient data management in the cloud.
A data lake is one central location that allows businesses to store all their structured (and unstructured) data at any scale. Data can be stored as-is, and without having to first structure the data users can run several types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning—to guide better decisions.
Eighty-seven percent of organizations fully utilizing their data lake have experienced improved decision-making ability. However, due to the boom in data, data lakes have become a dumping ground for multi-structured datasets that are difficult to get access to and value from. It is estimated that data generated globally is growing to 175 Zettabytes by 2025, which has created a data deluge that threatens to turn data lakes into data swamps that are increasingly expensive, complex, and difficult to scale. And when data increases in volume, it can be difficult to evaluate with traditional visual analytics and to spot key insights that people do not know to ask. As a result, it is cheaper to generate and store data than it is to transform it into actionable information because refining or restructuring data involves much more time and energy to compute.
This is where decision intelligence comes into play. Decision intelligence uses a unique approach that eases the burden of data analysis and empowers every individual to make insights-driven decisions. The technology can help organizations navigate and utilize their data lakes in new, organized ways, augmenting human decision-making with AI to get better, faster analytics.
To fully realize the benefits of the modern data stack and enhance their data lake investments, organizations can use decision intelligence to activate the data lake for real-time analytics. Specifically, organizations should use decision intelligence within their data lakes to:
• Simplify and speed up data analysis at scale
• Empowering business users and data analysts to iteratively explore data to identify trend drivers, uncover new customer segments, and surface hidden patterns in data
• Highlight changes in data—and the reasons behind those changes—to gain further insights
Driving Large-Scale Analytics
With extreme data growth comes many challenges, such as poor data quality, data silos, issues in scaling, improper integration, and organizational resistance. Today’s analytics tools unfortunately have taught people that the only way to deal with large-scale data sets is to spend endless amounts of time wrangling the data from its sources, replicating it, bringing it into a single place, and parsing it into bite-sized data sets. In fact, according to a report by ChaosSearch, IT teams are spending almost equivalent time preparing data (6.6 hours per week) as they are analyzing the data (7.2 hours per week).
Decision intelligence’s ML and AI capabilities make large-scale analysis easy. The technology actually thrives off of large data; by analyzing large-scale data, users can be assured that insights uncovered are more complete and patterns can be found across various sources of data. Decision intelligence combines traditional data querying and aggregation techniques with advanced ML-driven and statistical analysis to allow users to make faster, more informed decisions.
Empowering Business Users and Analysts
Unfortunately, many businesses don’t have sufficient resources to manage their enormous amounts of data well. According to the NewVantage Partners 2022 Data and AI Leadership Executive Survey, organizations continue to struggle to become data-driven, with only 26.5% reporting they have achieved this goal, and only 19.3% reporting having established a data culture. When combined with the reality of the growing technical skills gap, automation is one of the best answers for solving this problem and analyzing data at scale. Decision intelligence democratizes analytics for every user, making it possible for both analysts and business users to tap into the lake.
The combination of automation and natural language processing in decision intelligence makes it possible for anyone to connect, work with, and analyze data directly at the source, no matter their skill level. People in all departments can figure out why certain metrics have changed or why performance is down over a certain period. Decision intelligence helps teams quickly analyze all combinations of data within a data set, freeing analysts from manually generating their own hypotheses and writing individual queries that review only a subset of the data. This information can, and will, empower companies to fully utilize their data lake, simplifying the analysis process and making it faster to find more insights that were formerly left behind.
Gaining Deeper Insights
Decision intelligence can also provide answers to questions that teams don’t even know to ask. These modern analytics tools can spot the latest trends within a data lake at a granular level, identify reasons for those trends emerging, and then proactively provide those insights to teams. Results are explained to the user in plain terms, eliminating the need for them to spend time interpreting visualizations on their own. With this capability, more insight can be uncovered in less time, making complex data lakes a resource for quick, valuable analytics exercises.
Unorganized data sets are quickly becoming any company’s worst nightmare. Decision intelligence can add structure to lakes and warehouses, allowing businesses to gain greater value from them than ever before. With an augmented analytics tool in place, business users and data teams can more quickly and effectively analyze data residing in the lake to understand what is happening in their business, uncover the reasons behind why metrics change, and get recommendations on future decisions that can influence bottom lines.
About the Author
Ajay Khanna, CEO & Founder of Tellius, the AI-driven decision intelligence platform, is a tech entrepreneur who has a passion for building disruptive enterprise products with an awesome user experience. Prior to starting Tellius, Ajay was CTO and a founding member of Celcite, a telecom analytics and solutions company with $100MM+ in revenue, which was acquired by Amdocs. Ajay has more than 25 years of extensive experience working in various technical, business, and consulting roles. He holds a degree in electronics and communications engineering.
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