“It is a capital mistake to theorize before one has data.” – Arthur Conan Doyle, author of Sherlock Holmes
Despite the advice of Arthur Conan Doyle, theorizing to a greater or lesser extent is how 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 every front – from operation and engineering activities, to research and engagement strategies.
In fact, the data economy is already a multi-billion-dollar industry, generating employment for millions, and yet we’re only just beginning to tap its potential. It’s no accident that digital transformation is on every boardroom agenda. 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.
Big data is big business
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 in 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.
Data lakes have given rise to the concept of the “enterprise data bazaar”, a useful term coined by 451 Research. In the enterprise data bazaar, or marketplace, self-service access to data combines with data governance to produce a powerful platform that enterprises can use to steer the future direction of the business. You can read more – and find out how Infostretch measures up – in the 451 Research report, Getting Value from the Data Lake.
Drowning in data
Data lakes are not without their challenges. Gartner predicts 80 percent are currently inefficient due to metadata management capabilities that are ineffective.
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.”
Data engineering puts disparate data to work with agile analytics
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.
Extracting business value from these vast data volumes requires a rock-solid business strategy, a tried-and-tested approach, and deep technical and sector expertise. This is where firms with specialized digital experience, like Infostretch, have a unique role to play. We blend our agile expertise and data engineering successes with a wide array of technologies to process and extract value from data, quickly and in the right fit for your environment. We have broken down our proven approach to Data Engineering Services into four key phases for big data deployments:
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 solution architecture need to be assessed and defined according to the individual needs of the organization.
3. Develop & Operationalize: Infostretch works with clients to develop the technical approach for deploying and managing big data on-premise or in the cloud. The approach takes 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.
If you are looking to capitalize on your use of data, and you want to find out how to put data engineering to work in your organization, or if you have a specific data analytics challenge, contact us today.
Big data, data science, analytics, BI…data has been subjected to buzzword bingo over the years. Perhaps because of the hype, the results of big data initiatives often do not...
It seems counterintuitive that too much data could be a problem for the big data stack, but that's exactly the challenge that DevOps and DataOps teams are facing. When...
Data is the foundation for digital transformation, and data is what powers every sort of digital initiative. At times over-hyped, almost always under-used, data has steadily grown...