Companies That Learn to Navigate Data Lakes Will Lead
STORY INLINE POST
Economists love to say that only two things in life are certain: death and taxes. In the technology world, there is a third one: the business digital transformation is here to stay. Whether it’s a restaurant franchise improving “take out” order processing or an airline developing increasingly predictive algorithms to maximize dynamic pricing strategies, technology plus data lead the way.
Digital business transformation is growing increasingly faster through key technologies, such as advanced data analytics, machine learning, artificial intelligence (AI), and more automation. Companies that invest in digital transformation have understood that wins in business results driven by innovation far outweigh the investments made.
There is no doubt that artificial intelligence will transform every industry. According to McKinsey, netting out competition effects and transition costs, AI could create $13 trillion of GDP growth by 2030, most of which will be in logistics, education, agriculture, and manufacturing.
Truth be told, transformations are never easy. Oftentimes, IT infrastructure, databases, and software lack connectivity, appropriate data sharing protocols, and harmony among themselves, which explains why data lakes are at the core and forefront of digital transformations.
Enhanced data processing capabilities, database centralization and connectivity, and cloud-based analytics are all made possible through data lake blueprints and infrastructure. These central data repositories store all sorts of information, from client profiles, sales figures and product information, to transactional logs, pictures, audios, and videos. They also allow for different members of an organization to access information whenever they need to, under sound and transparent data governance policies.
A data lake makes it possible to centralize, analyze, and manage the information flow across the entire organization. Data arrives in all shapes and forms (text, images, audios, videos and, yes, the tabular data we are all too familiar with) and can be sifted, cataloged, and transformed, before it is exploited by the organization. From within a data lake, specific databases can be linked, analyzed, and consumed by algorithms to deliver competitive business advantages. At the end of this process, AI is able to improve critical decision-making, as it can help create true business value, from setting the right prices on consumer goods in online marketplaces and recommending shows for streaming services, to qualifying borrowers for the financial services industry.
At Intelimétrica, our clients seek advice on how to design, architect, and deploy comprehensive data lake strategies. The expected business result, at that point, is to unify a multiplicity of critical systems and eliminate information silos. Our data engineers make sure that every event and transaction within the organization is traceable, connected, and ultimately visible to the organization’s staff and leadership. Companies that center their digital transformation efforts on leveraging data gain visibility over performance, are able to identify deviations from expected behaviors, and can ensure timely execution of their business strategies.
The Four Stages of Data Lake Setups
The first stage of building a data lake is known as “Landing and Raw Data Zone.” At this stage, the data lake is not yet connected to core IT systems and serves as a “pure capture or ingesting” environment. At this point, data is neither classified nor structured.
The second stage is known as the “Data Science Environment,” where data is used for exploration and experimentation. Analysts have quick and easy access to data and are able to run trials that may lead to significant discovery of business value.
“Offload for Data Warehouses” is the third stage. At this level, data lakes become integrated with enterprise data warehouses (EDW). More data sets can be stored and data lakes can be used to extract detailed and specific information.
Finally, in the fourth stage, data lakes become a critical component of the organization’s operations. At this point, the organization is able to build and control data intensive applications, such as performance management dashboards, APIs, and microservices that exploit insights gained from data lake architectures.
Today, data lakes and advanced analytics are empowering companies all around the world, allowing them to understand, in near-real time, the critical events and patterns driving their performance. Into the future, companies will produce valuable business insights in short periods of time and will continuously improve and optimize their performance. These companies will become the leaders in their markets.