Why Big Data Project Management Can Be So Difficult

Big data is extremely beneficial to businesses, and gathering it is now easier than ever with today's technology. When it comes to the management of big data, therein lies the challenge. There is software now which can assist in tracking a company's customers and potential customers. It will collect information such as age, demographics, buying habits, and more. This data can help businesses design advertising campaigns, promote products to specific customers, discern personal habits and the best times to offer deals, and much more.

Along with the challenges of project management and handling big data solutions, new software is being designed all the time which helps companies with project tracking and data management. The project management software available today is superior to anything used in the past when it comes to amassing and analysing data. From there, the software can assist by giving businesses access to accurate estimates - for example when discussing projected earnings; reports by subject or category - such as median age of customers or geographic region which the largest sales of a certain product originate.

There are several reasons that managing big data remains a challenge. The five main challenges, as cited by Knowledge Integrity Inc, SAS, and other sources are as follows:


Which Technology to Choose

With so many competing technologies, how do you decide which one has the right applications for your needs? The human learning curve is such that companies will want the most effective solution with the least amount of new details to learn or protocols to set up.


Who Can Help?

Big data applications are growing steadily more appealing to the technological and business worlds. However, the people who know the most about harnessing the opportunities are in short supply. This is such a new field of interest that many technologically minded software and IT experts do not yet have a lot of experience in it.


How to Manage the Big Data

When gathering large amounts of information, what are the internet speed requirements? Storage requirements? What sort of maintenance may be needed for the vast databases of information? Knowledge about computer databanks, servers, storage solutions, and even all flash storage is just the starting point. With such a wide range of information available, someone new to the field may find its upkeep more difficult than projected.


Making Sense of the Big Data

After you have begun collecting the data, finding a way to quickly and efficiently sort through it is essential. Understanding what the numbers and information mean is vital to making big data work for you.


Identifying Quality in the Quantity

Discerning what information is useful from the information that is not useful is a huge task. This is why a specific plan for the management and maintenance of the big data gathering and storage is necessary. Data will need to be relevant as well as current, and the best way to ensure that is through stringent protocols implemented by the project manager.

Luckily there are solutions for making big data into a manageable tool. To address concerns of technology choice, look at the original purpose of the software applications. Visualise how this can work for your big data project. What are the strengths and weaknesses? Who will be available to offer support? When you can answer those questions, you will usually have success with the project implementation. Once your big data project has begun, you will also want to make sure that anyone who needs to work on it understands it well enough. Have a backup support person and policies for any necessary downtime.

By using your software as a starting point it will be easier to determine how fast your internet needs to be, how large your storage banks should be, and of course how much storage may need to be allocated. Basing those decisions on what the project hopes to achieve can sufficiently narrow the focus to a manageable level. This will also ensure that you have enough resources to make sense of the data you do receive and can always tell the quality from the quantity.

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