Over the years, the sheer amounts of data that companies have been responsible for storing, managing, organizing, and keeping it secure has continually risen exponentially. Sets of data that were once measured in gigabytes are now measured in esoteric terms such as terabytes, Exabyte, petabytes, and zettabytes – all of which measure amounts of data thousands or millions of times larger than the amounts that companies once had to deal with.
At some point in this exponential increase, it became clear that simply increasing the capacity of traditional storage methods would not be enough to store and utilize these new categories of a data. Since then the tech world has made efforts to develop entirely new methods for storing these new leagues of data. The near future will see even more trends emerge to help companies that handle these unprecedented volumes of data in a way that will keep them manageable and secure.
What is Big Data?
The term and the concept itself emerged in the early 2000s, when industry analyst Doug Laney defined Big Data as measured by three different markers – rather than just volume alone. Big Data presents challenges besides the sheer amount. According to Laney, the three challenges that define Big Data involve Volume, Velocity, and Variety.
Volume represents the inherent aspect that big data is named for: sheer size. Modern companies collect a vast amount of data, often automatically from web sources. Where storing it in the past would have been a huge problem to itself, new technologies have been created specifically to ease this burden.
Velocity represents the challenge of storing data at the same fast speeds it is coming in, from real-time sources.
Variety represents the many, often incongruous forms of data that companies need to figure out to deal with in conjunction with the Velocity and Volume aspects. The challenge is to find a way that databases, video, audio and other forms of data can be organized together – in high volume, and at high velocity.
The last ten to fifteen years have seen technologies like Hadoop helping to catch up with the needs of companies to store this vast, disparate, and ever increasing data. But as changes in information technologies continue, these needs become ever greater. What are some upcoming trends to help companies solve the Big Data problems of the present and the future?
Upcoming Big Data Trends:
Democratization of data stewardship: One trend that seems to be taking shape to become important in the coming years is the democratization of data position. This entails an increasing amount of control for end users over their own data, and opportunities to perform their own data integration. Both preparation tools as well as data lakes provide this opportunity to end users. This takes some of the role and responsibilities of stewardship away from companies themselves.
Consistent Semantic Metadata: Another trend involves the use of constant semantic metadata to make the data easier to find and use. Consistent semantics are the foundation of good data governance whether the architecture is in the cloud or on premise. Preparing data in a consistent way allows companies and users to later access and utilize the data they need – quickly and efficiently. Often, this consistency is achieved through an enterprise-wide data governance council. This council will monitor metadata so there is a consistent model applied across units.
Consistent modeling for Big Data: The trend toward an emphasis on Big data modeling is an extension of this trend, increasingly important in 2016. Applying these consistent models to the organization of Big Data is important at even a granular level to make data recycle smooth as a result, the data across different platforms like applications, business units can be maintained in good faith.These consistent data governance policies will be at the forefront of concerns in 2016 – whether a new system is being implemented, or an old one is being refined.
Application Data Management (ADM): It is yet another upcoming Big Data trend. Integrating multiple data management tools under one umbrella has been a challenge in the past for most companies. If done right, however, it can be one of the most effective ways for companies to manage data and make it accessible. Application Data Management can serve as a hub, enforcing data management policies across different applications and data consumers. This simplifies an important yet difficult task, and will likely become standard practice in the near future.
Link between Big Data to Transactional business data: An important trend to managing and especially utilizing Big Data in 2016 is that of linking Big Data to transactional business data. Tapping into the information contained in Big Data like data lakes can be extremely helpful to companies. For this to be effective though, it is key to consider the impact to the business long term, and how the data will become accessible to the business user. Setting specific business wide data collection and organization policies ensures that these processes support business users in the long term. Finding Big Data linking with transactional data will secure better business outcomes.
Use of Modern Data Governance Framework: Traditional master data management focuses on elements of master data such as customer and vendor etc. Today there are many new forms of data that companies need to manage. A rising trend, data governance 2.0, as it is called, goes beyond traditional MDM, connecting webs or preferred apps that can cover master, transactional and configuration data. Such approach tends to a centralization of data governance and allows all data sources to be integrated into a data governance strategy.
Automated and Regular Functional Apps: Another rising trend is simply the increased use of automated and off-the-shelf functional apps instead of massive system integrator custom implementations. Companies can take help of software automation without requiring external support for low-level needs and can use qualified resources for strategic advantages. In other words, software automation in 2016 will support company’s ability to scale these processes to their varying needs.
Increase in IoT: Big Data gathered by “IoT”can incorporate knowledge about your company’s inventory. In addition, customers, employees will engage in a digital way and traditional processes will have automation. Forbes says that by 2017, more than 30 percent of enterprise access to Big Data will be through intermediate data broker services, which will provide an environment to business decisions. It is believed that by 2020, 80% of business processes today will be either digitized or eliminated – in part, by real-time access to Big Data.
Conclusion:The trends on the horizon for Big Data in 2016 mostly involve simplification, digital automation, and a standardization of data practices across companies and across departments that will make it easier for Big Data to become useful to a wide variety of companies. These technologies and practices reflect the world in which Big Data is simply becoming the norm for companies dealing with data governance and organization. Data will become more accessible, the need for human labor will be reduced, and consistent practices across organizations will lead to a simplified handling of data. Surely, it will not be long before the term Big Data becomes outdated.