Article

Ilan Hertz
Ilan Hertz 22 August 2017
Categories B2B, Data & Analytics

Why Big Data Doesn’t Have to be a Big Deal: Handling Big Data from Multiple Sources in Analytics Projects

Big Data is bigger than ever. Beyond just visualizing how a business is performing, Big Data analytics (BDA) is increasingly becoming a major driving force behind the shaping of department-level strategies. And it’s not only enterprise-level companies that are reaping the rewards.

With recent advances in cost-effective, user-friendly platforms, ever-widening array of small and midsized organizations are also tapping into the competitive power that BDA brings—more focused marketing strategies, the creation of new revenue opportunities, improved customer service and streamlined operational efficiencies.

The outdated perceptions that analytics projects are overly complicated and expensive have long put off many organizations from investing in BDA. But as a growing number of industry leaders invest in dedicated analytics teams, organizations that avoid Big Data are increasingly finding themselves left behind.

What’s more, analytics platforms are becoming more affordable and accessible every year, making it easier for even small and midsized business to discover the “whys” behind their consumers’ habits, their inefficiencies, and – perhaps most importantly of all – the predictable trends that’ll be shaping their industry over the coming years.

Here are some of the main challenges your company may face when handling large sizes and multiple sources of data in analytics projects – and some tips for overcoming them.

Automating collection and organization of multi-source data

In a 2014 survey, the Aberdeen Group isolated a group of 89 organizations on the topics of information integration. The survey found not only that leading decision makers are incorporating more data sources than ever into their analysis, but that leaders are 126 percent more likely than followers to have automated processes for the capturing of data on an ongoing basis.

Since this wealth of automatically captured data comes from a wide range of sources, and is formatted in a wide variety of ways, it’s also mission-critical to index and classify new data sets in an automated way. In fact, Aberdeen’s survey found that leaders were 169 percent more likely than followers to have procedures in place for the automated indexing and classification of the data they’d gathered. Such procedures aren’t just for convenience’s sake – leaders agree that they frequently shave days or even weeks off project completion times.

Ensuring high data quality and security throughout your pipeline

It’s an uncomfortable but unavoidable truth that big data is dirty data. This is especially true when you’re assembling data from multiple sources – each dataset you’ve assembled probably comes in a different format, includes a somewhat different range of attributes, and arranges those attributes in a different way. The task of turning all these disparate datasets into clear insights can sometimes seem overwhelming.

This is why it’s crucial to perform some preliminary cleanup on your data before you start applying analytics to it. If your data set includes duplicate entries – or worse, data that’s logically conflicting or missing – this can lead to serial errors throughout the analytics processes, resulting in inaccurate visualizations and flawed data-driven decision-making. Therefore, careful data preparation is essential for accurate insights, all the way down the processing pipeline.

It’s not just human error that can contaminate a data set. A strong security system is an absolute prerequisite for any company with stored data. As a recent cyber attack on the UK’s National Health Service demonstrated, a security breach can lead to corrupted and missing data throughout your organization’s infrastructure, even for large, well-defended organizations. So, when choosing your data warehouse, make sure it offers best-practice security measures at the system, object, and data levels.

Focusing on data subsets that lead to actionable insights

When working with a complex dataset assembled from multiple sources, it’s not always clear where to start looking for insights that can benefit your business. In fact, without a precise overall strategy, you’ll have no assurance you’ll be getting any return on your Big Data investments. This is why it’s essential to make sure your business has a clearly communicated, shared vision about what to do with the data you gather, so you’ll have some idea about where to point your analytics tools first.

Start simple, and prioritize the segments of your database that’ll be easiest to comb for actionable insights. That often means investigating web traffic and clickstream stats you’ve pulled from Google Analytics, or customer transaction history from your point of sale (POS) system – where you can often discover trends you might not have noticed before. Maybe you’ve got a product that sells well no matter how you price it – an insight that could immediately increase your revenue once you apply that piece of learning.

One of the most effective techniques for discovering these kinds of insights – and presenting them to decision makers in your organization – is to utilize  interactive data visualization tools. The latest generation of BDA platforms include powerful toolkits for turning data into easily comprehensible charts, graphs and other visualizations. In fact, organizations that use data visualization are 63 percent more likely make smarter business decisions and 60 percent less likely to receive critical information after it’s too late.

The world’s leading organizations in many industries have already invested in dedicated teams of data scientists and analytics experts to integrate and analyze data from multiple sources. However, highly trained experts demand high salaries – and even in-house training programs can be costly and time-consuming. By investing in a cost-friendly platform, which streamlines data analytics, you’ll be much better equipped to bring together complex multi-source data, and begin analyzing it right away.

But a dashboard software platform alone isn’t enough. The most powerful technology is only useful when integrated into an organization-wide strategy that includes automated data gathering and processing, data cleanup and ongoing security, and a results-oriented approach to selecting datasets for analysis. Only when your organization brings all these components together into a single unified approach will you begin to overcome the challenges of handling multi-source data, and truly realize the benefits a multi-source database has to offer.

Naveen Sharma
Naveen Sharma

Good article Ilan. Just wondering when we collect different data from various sources (within the organisation); with user consent for each dataset, what safeguards should we build in to protect privacy of individuals. i.e. when all datasets are in a single digital repository, how can we ensure that we can't create the full identity of an individual using BDA? Assuming no de-identification of data and when we collect data; each source will not have all details about an individual. Data examples are: web usage; wireless access; location information and single sign on details. I am thinking more in line with some form of data governance and ethical considerations?

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