"Data! Data! Data! I can't make bricks without clay." – Arthur Conan Doyle, author of Sherlock Holmes

Despite Arthur Conan Doyle's words, making bricks without clay is the way the majority of business has been conducted until the digital age. Whether you call it gut instinct or business smarts, the ability to spot trends and anticipate demand gives companies the edge over the competition.

Now the digital age is taking the guesswork out of the process. Data is redefining decision-making on every front – from operations, R&D, engineering right through to go-to-market engagement strategies.

The data economy is already a multi-billion-dollar industry, generating employment for millions, yet we are only just beginning to tap its potential. Digital transformation is on the agenda in every boardroom, the majority of businesses use some level of AI and analytics and automation are becoming a routine part of many business areas. The secret to unlocking future prosperity in almost any business, whether established or a digital native, lies with the data.

Today, the key to successful business decision-making is data engineering.

Delivering on data-based decision-making

2.5 quintillion bytes of data are generated every day on the internet!

And that figure is growing. So is the desire to put it to good business use. Utilizing vast repositories for storing data, otherwise known as data lakes, is now commonplace. These differ from traditional warehousing solutions not least because they are cloud-based, but also that they aim to present the data in as "flat" a structure as possible, rather than in files and sub-folders, and in their native format as well. In other words, data lakes are primed for analytics.

Organizations that are used to data warehouses may be wondering if data lakes will take their place. The short answer is no, most will require both as they are used for different purposes. Because data lakes are well suited to many types of advanced analytics, they are becoming an increasingly key component for organizations serious about extracting value from their data.

Not waving, but drowning in the data lake

Data lakes are not without their challenges. Gartner predicts 80 percent are currently inefficient due to ineffective metadata management capabilities.

IDC's Ritu Jyoti spells it out for enterprises, noting, "Data lakes are proving to be a highly useful data management architecture for deriving value in the DX era, when deployed appropriately. However, most of the data lake deployments are failing, and organizations need to prioritize the business use case focus along with end-to-end data lake management to realize its full potential."

Putting data to work

When we talk to customers, the business drivers for data engineering are clear. Businesses are crying out for quick access to the right data. They need relevant reports, delivered fast. They want to be able to analyze and predict business behaviors, and then take action in an agile fashion. Data growth shows no signs of slowing, and the business insights enterprises will gain are only as good as the data they put in. As data sets grow, enterprises need to be able to quickly and easily add new sources. Finally, efficiency is a consideration since the cost of data systems, as a percentage of IT spend, continues to grow.

Infrastructure designed with data in mind

Getting their data in good order should be the number one priority for enterprises. Why? The fallout of the pandemic has meant there is increased pressure to do more with less. AI and data analytics can help businesses speed up processes, improve insights and drive efficiency, but the right foundation needs to be in place. When we break down any big data deployment, it generally falls into four distinct phases:

1. Assess & Qualify: First, the focus is on understanding the nature of the organization's data, formulating its big data strategies and building the business case.

2. Design: Next, big data workloads and the solution architecture need to be assessed and defined according to the individual needs of the organization.

3. Develop & Operationalize: Here, organizations develop the technical approach for deploying and managing big data on-premise or, increasingly, in the cloud. This phase must take into account governance, security, privacy, risk, and accountability requirements.

4. Maintain & Support: Big data deployments are like well-oiled engines, and they need to be maintained, integrated and operationalized with additional data, infrastructure and the latest techniques from the fields of analytics, AI and ML.

Agile analytics help organizations become more nimble, competitive, resilient and efficient. Data engineering is the first step in the journey to unlocking the benefits that data holds. Little wonder, then, that there is currently high demand for data engineering services as businesses come to realize the importance of getting their data in order first. Extracting value from vast data volumes is fast turning into the number one competitive business differentiator.

About the author:

Deven Samant is Head of Enterprise Data and Cloud Practice at Infostretch, a Silicon Valley digital engineering professional services company.  www.Infostretch.com (17) Deven Samant | LinkedIn Twitter: @finddevanatis @infostretch Blog | Digital Engineering, Digital Technology Solutions & Services Firm – Infostretch