One of the most rewarding aspects of my job is being able to see what the world’s most advanced organizations are doing with new data technologies. However, it can sometimes be tragic to witness large organizations make and execute against assumptions related to big data technology that nearly guarantee their failure. With data lakes being so new to organizations, an early failure can significantly set back the opportunity to fundamentally transform analytics. However, while the potential for big data innovation is significant, organizations are mired with slow, manual, and time-consuming processes for managing the tasks that turn raw big data into relevant business assets and insights. Without addressing these challenges in a systematic way, organizations find that data lake projects turn into labor-intensive, complex endeavors.
The fundamentals of diving into data lakes: Tips to ensure your organization will swim
Let’s take a step back to understand the core best practices that distinguish organizations that have been successful with data lakes.
Ground data lake projects around clear target business outcomes and business context
How can any organization be successful with a data lake project without establishing how this “success” will be measured? It is almost impossible to ensure that the data lake can be trusted without grounding the data first, by providing the business context behind it. Establishing a business glossary and technical taxonomy up-front provides organizations with a constitution, ensuring crystal clarity around the meaning and relevance of all data in the data lake. These business definitions are even more critical the more complex the data is. The absence of context increases the risk of incomplete, inconsistent, inaccurate, insecure, and incompliant data – both with internal controls and external regulations. Well-defined business goals and business context can dramatically streamline and accelerate data lake tactics – while ensuring that projects deliver incremental value.
Adopt an agile development process to ensure collaboration across the data lake ecosystem
With business conditions changing so rapidly, organizations that do not deliver incremental value quickly risk losing political capital or misaligning with the dynamic requirements of the business. Rather than relying on the antiquated waterfall process model to synchronize with executives and cross-functional champions, it’s important to leverage agile development methodologies. An agile approach encourages frequent, cross-functional collaboration and ensures alignment with changing goals.
Use systematic, intelligent data management practices
Manual processes are not only inefficient, but they also pose significant risks for businesses. Technical debt from hand-coded scripts and manually enforced policies can incur huge costs for businesses over the long run – and inhibit the long-term sustainability of projects. The manual approach to data management cannot seamlessly scale, given the overwhelming volumes and variety of data available to organizations today. Discovering what raw data exists in a data lake can be an unsurmountable challenge without a technology-driven strategy for leaning on artificial intelligence capabilities to discover and interpret the data. Taking an artificial intelligence-powered strategy can also proactively identify correlations and similarities between different data assets to help build holistic, end-to-end views of data assets for data stewardship.
Once a holistic view of data assets is achieved, businesses can identify pre-inferred relationships between all data sets. This detection empowers business users to tap into new data assets that may be of interest to them. For both risk mitigation and opportunistic reasons, a systematic approach – based on artificial intelligence-driven data management – clearly enables more value to be derived from data lakes, while ensuring that success is not limited by recruitment and retention of narrowly specialized experts.
Ensuring data lake success through clarity
Taking an artificial intelligence-powered approach to data management is the more repeatable and sustainable path to ensuring data lake success. A comprehensive, automated strategy ensures that amidst growing volume and variety of big data, organizations can still find, prepare, cleanse, master, govern, and protect their assets. By adding in artificial intelligence, businesses can ensure that organizations can systematically find any data, discover data relationships that matter, quickly prepare and share the right data with the right people at the right time – and ultimately deliver more innovative, timely, relevant, and personalized digital experiences. Artificial intelligence technology helps organizations deliver against business expectations – no matter how fast data volumes grow, how complex the data model is, and whichever data sources need to be integrated.
Automation transformation: Man versus machine to man with machine
Data assets are growing at a rate of at least 40 percent per year – and can no longer be managed with a manual process. With IT headcount under constant pressure, it is an unfair assumption that manual processes can effectively keep up with the overwhelming volume and variety of data that organizations utilize. Hinging a big data strategy on the ability to recruit and retain specialized development resources is a risky path, if not a surefire route to failure.
To solve this problem, organizations must embrace automation. Invoking the power of the machine to analyze the structure of data significantly reduces the amount of manual effort dedicated towards understanding and utilizing data. Looking ahead, automation also ensures that data management remains scalable and sustainable for the future.
When diving into data lakes, leveraging artificial intelligence is undoubtedly the best bet to ensure that your organization will swim. Artificial intelligence unleashes the power of data lakes – leading organizations to realize new growth opportunities through innovation that lead to intelligent market disruptions, without relying on armies of specialized developers to get you there. Organizations that perceive artificial intelligence technology as a life preserver when navigating their data lake management strategy are sure to achieve business benefits – despite outside conditions.