The day an IBM scientist invented the relational database in 1970 completely changed the nature of how we use data. For the first time, data became readily accessible to business users.Â Businesses began to unlock the power of data to make decisions and increase growth. Fast-forward 48 years to 2018, and all the leading companies have one thing in common: they are intensely data-driven.
The world has woken up to the fact that data has the power to transform everything that we do in every industry from finance to retail to healthcareâ if we use it the right way. And businesses that win are maximizing their data to create better customer experiences, improve logistics, and derive valuable business intelligence for future decision-making. But right now, we are at a critical inflection point. Data is doubling each year, and the amount of data available for use in the next 48 years is going to take us to dramatically different places than the worldâs ever seen.
Letâs explore the confluence of events that have brought us to this turning point, and how your enterprise can harness all this innovation â at a reasonable cost.
Todayâs Data-driven Landscape
We are currently experiencing a âperfect stormâ of data. The incredibly low cost of sensors, ubiquitous networking, cheap processing in the Cloud, and dynamic computing resources are not only increasing the volume of data, but the enterprise imperative to do something with it. We can do things in real-time and the number of self-service practitioners is tripling annually. The emergence of machine learning and cognitive computing has blown up the data possibilities to completely new levels.
Machine learning and cognitive computing allows us to deal with data at an unprecedented scale and find correlations that no amount of brain power could conceive.Â Knowing we can use data in a completely transformative way makes the possibilities seem limitless.Â Theoretically, we should all be data-driven enterprises. Realistically, however, there are some roadblocks that make it seem difficult to take advantage of the power of data:
Trapped in the Legacy Cycle with a Flat Budget
Â The âperfect stormâ of data is driving a set of requirements that is dramatically outstripping what most IT shops can do. Budgets are flat âincreasing only 4.5% annually â leaving companies to feel locked into a set of technology choices and vendors. In other words, theyâre stuck in the âlegacy cycleâ.Â Many IT teams are still spending most of budget just trying to keep the lights on. The remaining budget is spent trying to modernize and innovate, and then a few years later, all that new modern stuff that you brought is legacy all over again, and the cycle repeats. Thatâs the cycle of pain that weâve all lived through for the last 20 years.
Lack of Data Quality and Accessibility
Most enterprise data is bad. Incorrect, inconsistent, inaccessibleâ¦these factors hold enterprises back from extracting the value from data. In a Harvard Business Review study, only 3% of the data surveyed was found to be of âacceptableâ quality. That is why data analysts are spending 80% of their time preparing data as opposed to doing the analytics that weâre paying them for. If we canât ensure data quality, let alone access the data we need, how will we ever realize its value?
Increasing Threats to Data
The immense power of data also increases the threat of its exploitation. Hacking and security breaches are on the rise; the global cost of cybercrime fallout is expected to reach $6 trillion by 2021, double the $3 trillion cost in 2015. In light of the growing threat, the number of security and privacy regulations are multiplying.Â Given the issues with data integrity, organizations want to know: Is my data both correct and secure? How can data security be ensured in the middle of this data revolution?
Vendor Competition is Intense
The entire software industry is being reinvented from the ground up and all are in a race to the cloud. Your enterprise should be prepared to take full advantage of these innovations and choose vendors most prepared to liberate your data, not just today, but tomorrow, and the year after that.
Meet the Data Disruptors
It might seem impossible to harness all this innovation at a reasonable cost. Yet, there are companies that are thriving amid this data-driven transformation. Their secret? They have discovered a completely disruptive way, a fundamentally new economic way, to embrace this change.
We are talking about the data disruptors â and their strategy is not as radical as it sounds. These are the ones who have found a way to put more data to work with the same budget. For the data disruptors, success doesnât come from investing more budget in the legacy architecture. These disruptors take an approach with a modern data architecture that allows them to liberate their data from the underlying infrastructure.
Put More of Your Data to Work
The organizations that can quickly put right data to work will have a competitive advantage. Modern technologies make it possible to liberate your data and thrive in todayâs hybrid, multi-cloud, real-time, machine learning world.Â Here are three prime examples of innovations that you need to know about:
- Cloud Computing: The cloud has created new efficiencies and cost savings that organizations never dreamed would be possible. Cloud storage is remote and fluctuates to deliver only the capacity that is needed. It eliminates the time and expense of maintaining on-premise servers, and gives business users real-time self-service to data, anytime, anywhere. There is no hand-coding required, so business users can create integrations between any SaaS and on-premise application in the cloud without requiring IT help. Cloud offers cost, capability and productivity gains that on-premise canât compete with, and the data disruptors have already entrusted their exploding data volumes to the cloud.
- Containers: Containers are quickly overtaking virtual machines. According to a recentÂ study, the adoption of application containers will grow by 40% annually through 2020. Virtual machines require costly overhead and time-consuming maintenance, with full hardware and operating system (OS) that needs managed. Containers are portable with few moving parts and minimal maintenance required. A company using stacked container layers pays only for a small slice of the OS and hardware on which the containers are stacked, giving data disruptors unlimited operating potential, at a huge cost savings.
- Serverless Computing: Deploying and managing big data technologies can be complicated, costly and requires expertise that is hard to find. Research by Gartner states, âServerless platform-as-a-service (PaaS) can improve the efficiency and agility of cloud services, reduce the cost of entry to the cloud for beginners, and accelerate the pace of organizationsâ modernization of IT.â
Serverless computing allows users to run code without provisioning or managing any underlying system or application infrastructure. Instead, the systems automatically scale to support increasing or decreasing workloads on-demand as data becomes available.
Its name is a misnomer; serverless computing still requires servers, but the cost is only for the actual server capacity used; companies are only charged for what they are running at any given time, eliminating the waste associated with on-premise servers.Â It scales up as much as it needs to solve that problem, runs it, and scales it back down, turns off. The future is serverless, and its potential to liberate your data is limitless.
Join the Data Disruptors
Now is the time to break free from the legacy trap and liberate your data so its potential can be maximized by your business. In the face of growing data volumes, the data disruptors have realized the potential of the latest cloud-based technologies. Their business and IT teams can work together in a collaborative way, finding an end-to-end solution to the problem, all in a secure and compliant fashion. Harness this innovation and create a completely disruptive set of data economics so your organization can efficiently surf the tidal wave of data.
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