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 in importance. The well-worn phrase “Data is the new oil” sums up the shift in how we perceive it. Just as oil transformed economies last century, so data is now transforming established sectors and forging new ones. In fact, Forrester attributes a $1.2 trillion competitive advantage to data-savvy companies over their less-informed peers by 2020.
Five years ago, the World Economic Forum described data as holding value much like any other economic asset. Since then organizations have focused on harnessing data to maximize value, but it’s not always an easy process.
Firstly, there is so much of it! IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1ZB of data generated in 2016. These are astonishing quantities and so it is no surprise when studies suggest that the lion’s share of data goes unused. Figures vary according to the type of data, but roughly speaking, Forrester estimates organizations only make use of around a third of what is available to them.
Partly that’s down to the difficulty of “seeing” the data when it takes so many disparate forms and resides in so many places. We’re talking the whole range of enterprise data, from customer-related SaaS data and operational data right through to social media and even emails, contacts and web logs. There are added complications too from the regulations surrounding data security, privacy, industry-specific standards and laws, as well as the cost of all storage and maintenance, which nowadays is almost always in disparate places onsite as well as in the cloud.
Data is transformative, when used the right way. It can change businesses, inspire new business models, confer competitive edge, reduce costs and streamline systems and processes. Organizations increasingly require a holistic view of all this information. But more than that, they need strategies and processes to extract real business insights and turn these into actions. Data engineering is the foundation for data analytics and data science. It ensures that management information systems (MIS) and data scientists have the right data to build on.
That’s why Infostretch is launching Data Engineering Services, a new suite of big data services to help organizations turn this disparate data into actionable intelligence. Infostretch’s tried-and-tested methodologies use the latest agile analytics techniques to reduce deployment risks and accelerate time-to-value, whether the end objective is to launch a product or service to market quicker, or extract value from investments faster. It’s a proven process, and in this blog we’ll show you how it unfolds.
1. Understand the Scope
We first conduct an assessment to understand and qualify the nature of an organization’s data. There are different strategies involved in putting big data to work, but most importantly we ensure that we have built a rock-solid business case for big data. This guarantees your business will gain real, actionable insights at the end of the process.
2. Design the Solution
We then get into the nitty-gritty of assessing Big Data application workloads and defining solutions architectures based on an organization’s needs. When we worked with a leading digital health company to design its flagship health monitor, we understood the importance of processing a high rate of data. Consequently we designed a solution that included micro-batch analytics to help the company cope with very high transaction rates.
3. Develop a Blueprint
Next, we develop a technical approach for deployment and management on either a private data center or in the cloud, leveraging our ecosystem of industry partnerships. For instance, because we participate in the Amazon Web Services Partner Network (APN) on both a consulting and technology basis, we can leverage our expertise on the AWS platform to help customers take full advantage of all its capabilities to optimize data analytics.
4. All Systems Go!
Getting a deployment up and running also involves addressing all the governance, security, risk and accountability requirements inherent in handling data. More than that, Infostretch understands that a smooth roll-out depends on people being on-board with the solution, and that future management and ongoing success is actually as much about winning hearts and minds as about quick results.
5. Keep on Delivering Value
Truth is, deploying, integrating and operationalizing cloud-based big data infrastructure isn’t a “switch-it-on-and-forget-about-it” solution. We help companies maintain and support their Big Data investments, or we make sure our customers are equipped to do so themselves. This was the case with a leading location services provider, where we built a data aggregation solution. Our platform-agnostic approach meant the solution could be deployed across different cloud providers and could be updated and scaled easily by their own developers.
Do you want to find out how data engineering can transform your organization’s data into actionable intelligence? So do we! Infostretch has designed a free, no obligation assessment so organizations can get the measure of their data engineering capabilities and even find out the steps they can take to advance. Schedule your free consultation today.
When we discuss apps or other digital initiatives, it’s usually in the context of improving their build and the customer outcomes from them. We tend to treat each app as a...
It has been a big week in tech. Not only did we have the Google I/O keynote yesterday (last week’s blog covered that in more detail), but also Kleiner Perkins Caufield &...
With Super Bowl around the corner, companies are all set to kick-off their million dollar commercial to get attention during the event. But sadly, these commercials do nothing to...