3 Big Data Challenges Your Business Will Face in 2017
One of the biggest challenges companies face is balancing competing demands, such as security versus accessibility, data management, and data mining. These problems are only compounded by the sheer amount of data businesses are collecting; we are collecting masses of data without clear understanding regarding how it is used to improve the bottom line and suffer impaired productivity trying to sort through it all. This isn’t the only problem companies face today. Here are the four Big Data challenges your business will face in 2017.
While computing power doubles about every eighteen months according to Moore’s Law, the amount of data we generate is increasing 40 to 60 percent per year, depending on the business. This is separate from the indexes used to try to track the files where the information is stored and backups of the backups demanded by various government regulations or contractual requirements.
Companies are considering “data lakes” that store masses of data in unstructured formats, but now the problem of finding and converting data to usable information becomes even bigger.
Making Use of Data
Data takes the form of raw numbers and entries. Information is what you have when data has meaning attached. Knowledge occurs when you can use information to your benefit and we are throwing so much data at knowledge workers they are struggling to make use of it. Businesses face the challenge of finding people who can mine big data to generate the information knowledge workers need to make good decisions at the time they need it. This is a problem foreseen by management guru Peter Drucker, but at a far greater scale than what he imagined.
This challenge is multiplied by the various locations where data resides and conflicting data formats. And that is why Big Data analytic experts are paid such high salaries. The democratization of predictive analytics could potentially reduce this problem, but that is still in the future for most business cases, since data is becoming both more complex and disparate.
Security is a major problem for big data companies. The cloud storage used to host the masses of data at a low cost per terabyte also gives many different people access. Even when the cloud server is relatively secure, backups and backups of backups to minimize disruption have to be protected and not always are. With the internet of things, security is often a distant third or fourth concern compared to data collection and sharing, though this information can be used against consumers.
Security can also be difficult when recovering lost data. Unfortunately, third-party data recovery services absolutely have to have access to crucial information when performing data recovery. This is why you should only work with SSAE 16 certified data recovery service such as Secure Data Recovery. You should also consult a secure data recovery expert from the start when setting up your databases.
Data storage has long been a problem for companies. Technology has kept up to date, but the internet of things and demands for 100% uptime make this a greater problem. Mining the massive, disparate, scattered and complex data sets to make use of it remains a serious challenge. This is in conjunction with the significant resources necessary to perform data mining. Securing data across many different platforms is an ongoing issue that is all too often neglected.