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.
Data Storage
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
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.
