Growth in the Data-Driven Enterprise
One of the most significant and exciting movements emerging is that enterprises across a wide range of industries-from finance and healthcare to technology, retail, and more-are becoming more and more data-driven. That’s because the world that we live in is increasingly data-driven. In this new reality, what separates the most successful organizations from the pack (regardless of industry) comes down to the effectiveness with which they leverage their data.
Two major drivers of this trend are the ever-escalating amount of available data, alongside the rise of extremely data-driven organizations across all sectors. And while it comes with its share of challenges in terms of data management, regulation, and security (see below), the growth of the data-driven enterprise benefits businesses as well, allowing them to be more innovative and disruptive, as well as ultimately more effective and competitive. In short, aligning big data with business strategies helps to ensure that customers receive the most tailored service. In this new reality, winning companies will be the ones that turn their data insights into improvements and innovations for customers.
Increased Interest in Risk Analytics and Compliance
The new European Union privacy regulation-the General Data Protection Regulation (GDPR)-is preparing to take effect in 2018. GDPR and other regulatory regimes will cause many organizations to ramp up the attention that they pay to risk analytics and compliance in the coming year. Companies need to understand their exposure to risk and manage their risk tolerance, particularly in the area of regulatory compliance. Smart use of risk analytics can help companies try to predict their risk level and create a strategy based on insights gained from data, per the growth of the data-driven enterprise.
In the case of GDPR, it’s important to note that this may affect your business even if your company is not based in the EU. The new regulation will apply to any entity that offers goods or services to EU subjects, and/or monitors data related to EU subjects. What matters is where a person (or data subject) is located whose data is being processed. When you keep in mind the steep fines and penalties that can be levied against organizations that fail to comply with GDPR and other local and global regulations, it becomes clear why risk analytics need to be on the front burner in 2017.
More Control and Balance of Data Prior to Ingestion
In light of the trends above, it makes sense that enterprises also need to embrace more structured data planning and management. With the explosion in data, it is no longer practical to simply throw all the information in without a plan; companies are newly challenged in 2017 to have a plan for use in place on how to use that data, as well as how to securely retain and archive it.
This is the year that businesses will be looking for ways to take complete ownership of their sensitive data across all source types, whether big data platforms, relational databases, or structured/unstructured data repositories. The ideal data-monitoring solution will offer a way for users to see quickly at a glance what, where, and how sensitive data is being detected, protected, and monitored across the enterprise via a visual dashboard. Such a dashboard allows IT administrators to have oversight that extends to on premise and in the cloud. The goal of every enterprise in 2017 should be to have a comprehensive, streamlined strategy to provide sensitive data security, privacy compliance, and risk mitigation.
Increased Urgency to Address Potential Data Breaches
Fear of a data breach is the biggest barrier that can keep companies from realizing the benefits of big data and the cloud. We’ve all seen the disturbing new stories about the rise of cybercrime across diverse industries. From ransomware hacks to insider file tampering, organizations face a mounting urgency to address this challenge from both a prevention and a preparation standpoint once a breach happens.
With that in mind, data-centric security becomes imperative to protect data against breaches. Enterprises need to continue to figure out ways to truly guard and secure their data, as opposed to just their systems. The type of solution needed in 2017 is one that can help companies detect where sensitive data resides, understand who is accessing it, monitor it in real time, and protect it with encryption and masking strategies.
Increase in Machine Learning and Cognitive Computing
As enterprise intelligence continues to evolve, machine learning and cognitive systems become more vital. Machine learning-which is a branch of artificial intelligence-is the design of applications or systems that can learn based on the data that’s input or output.
How do machine learning and other forms of cognitive computing relate to big data? Again, these trends are all linked when you recognize that the huge influx of terabytes upon terabytes of data is difficult for even the most robust data analyst to handle unaided. Even with a wide range of statistical tools to help information scientists dig into data, it is no cakewalk. Machine learning gives data scientists another way to really mine the data for patterns-and because of this fact, more industries will take advantage of these benefits in 2017, to help them make the most of the volumes of information that they are now charged with understanding.