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4 Ways Disparate Data Sets Are Holding You Back

As an enterprise with a lot of different sectors and moving parts, having disparate, siloed data is hard to avoid. After all, the marketing department may deal with certain information while the IT team works with other data. The details the finance department leverages aren’t the same as what’s used by HR, and so on. However, when this information exists in separate silos and never fully comes together, it could be holding your organization back considerably, particularly when it comes to your big data initiatives.

Today, we’ll look at just a few of the ways disparate data sets could be a problem for today’s companies, and how your business can help address this prevalent problem.

1) A world of enterprise apps

One of the biggest sources of disparate data is the range of business applications employee users leverage. While these activities may take place under the watchful eye of the IT team, each application will contain information unique to that platform and if this data isn’t brought together at some point, it can create skewed analytics results.

According to Cyfe, the average small business utilizes just over 14 different applications. This number jumps to 500 when examining large enterprises.

“[T]he more apps your organization uses the harder it is to make data-driven decisions,” Cyfe noted in a blog post. “Why? Because keeping a pulse on your business’ sales, marketing, finances, web analytics, customer service, internal R&D, IT, and more as isolated sources of data never gives you a complete picture. In other words, big data doesn’t lead to big insights if you can’t bring it together.”

2) Stuck in the information-gathering phase

It’s not only the location of data that can cause an issue – the sheer volume of information can also create significant challenges, particularly when an organization is working to gather all of that information in a single place.

“It can take considerable time to bring this information together without the right resources.”

Informatica pointed out that that as much as 80 percent of an analytics initiative involves the actual collection of information in order to establish a bigger, better picture for analysis. However, when a large number of details are located in several different locations, it can take considerable time to bring this information together without the right resources. What’s more, as the company is working to pull data from different sources, new, more relevant information is being created that will further impact analysis.

In this type of environment, it’s easy to get stuck in the gathering phase, where data is constantly being collected, while the team doesn’t move on to the analysis part of the initiative as quickly as they should.

3) Fear of missing out: Reducing repetition

This leads us to the next issue: fear of missing out. Because big data is constantly being created and changing so quickly, businesses may hesitate to analyze and leverage the insights due to a fear of missing out on the next piece of data that is just coming to light.

Furthermore, Informatica noted that when data isn’t organized and kept in several different locations, it can cause problems on not just one, but a number of analysis initiatives, as employees will have to repeatedly pull these details, wasting time and effort.

“The key to minimizing repetitive work is finding a way to easily reuse your logic on the next data set, rather than starting from square one each time,” Informatica pointed out.

This is only possible, however, with the right big data platform that can help gather information from all disparate sources in the shortest time possible. In this way, businesses can eliminate costly repetitive processes while still ensuring that nothing falls through the cracks as information is gathered for analysis.

4) Missing information: Is it possible to know what isn’t there?

Siloed data can also lead to gaps in knowledge, which can considerably impact analysis results. For instance, a company seeking to learn more about their client base may include a range of different data sources, but may overlook details in the customer relationship management solution, causing them to miss important insights about brand interactions. While this is an extreme example, it helps illustrate the pitfalls of incomplete data sets.

Addressing disparate data: Partnering for success

These are challenges that can affect businesses in every sector, but can be easily and expertly addressed when companies partner with a leading big data solution provider like Aunalytics. Aunalytics has everything your enterprise needs to fully support its big data initiatives. Our unique, best-of-breed technology, Aunsight, ensures that information is gathered from all disparate sources, and that analysis is always as complete as possible. We help you collect and integrate your data so that workflows and unique algorithms can be established, leading to the most high-quality, actionable insights.

Customer Intelligence

What is Little Data and what does it mean for my big data initiatives?

Big data has been the buzz of the business world for years now, with businesses across every industrial sector gathering and analyzing information in an effort to leverage the resulting actionable insights. For the past few years, “big” has been the name of the game, with organizations working to indiscriminately collect as many details in a whole host of different areas.

Now, however, a new strategy is coming to light: little data. But what, exactly, is little data? How is it related to big data? And how can this approach make all the difference for your business?

Little data: A definition

Little data comes in almost exact contrast to big data, but can also be complementary – and very important – to supporting a big data strategy.

According to TechTarget, little, or small, data are more selective pieces of information that relate to a certain topic or can help answer a more specific pain point.

“Little data comes in almost exact contrast to big data.”

“Small data is data in a volume and format that makes it accessible, informative and actionable,” TechTarget noted.

The Small Data Group further explains that little data looks to “connect people with timely, meaningful insights (derived from big data and/or ‘local’ sources) organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.”

A step further: What’s the difference?

The key differences here are demonstrated by big data’s defining characteristics. Big data, as opposed to little data, is often defined by what are known as the three V’s, including volume, variety and velocity. The first two are particularly important here. Whereas big data usually comes in the form of large volumes of unstructured or structured information from a range of different sources, little data simply doesn’t cover as much ground.

Little data, on the other hand, comes from more precise sources and will include a smaller amount of information in order to address a previously defined problem or question. Where big data is vastly collected and then analyzed for insights that might not have been accessible previously, little data is gathered and analyzed in a more specific way.

Forbes contributor Bernard Marr noted that little data typically includes more traditional key performance metrics, as opposed to large, indiscriminate datasets.

“Data, on its own, is practically useless. It’s just a huge set of numbers with no context,” Marr wrote. “Its value is only realized when it is used in conjunction with KPIs to deliver insights that improve decision-making and improve performance. The KPIs are the measure of performance, so without them, anything gleaned from big data is simply knowledge without action.”

Little data and big data: Working in tandem

However, this is not to say that little and big data cannot work together. In fact, little data can help bring additional insight and meaning to the analysis results of big data.

For instance, a big data analysis initiative could show certain patterns and facts about a business’s customers. Little data can then bring even more to the table, helping to answer more specific questions according to key performance indicators.

These KPIs can also be utilized to measure an organization’s ability to put its big data insights to work for the business.

“For example, a retail company could use a big data initiative to develop promotional strategies based on customer preferences, trends and customized offers,” Marr noted. “But without traditional KPIs such as revenue growth, profit margin, customer satisfaction, customer loyalty or market share, the company won’t be able to tell if the promotional strategies actually worked.”

Little data in action

Little data can also be more personal in nature, offering knowledge and actionable insights for a company’s consumers. Nowhere is this more prevalent than in the fitness industry, particularly with the popularity of wearable fitness monitors that sync to a user’s mobile device.

Harvard Business Review contributor Mark Bonchek noted that oftentimes, little data pertains to each consumer as an individual, and that these details are what companies seek out to utilize as part of their big data strategies.

“Big data is controlled by organizations, while little data is controlled by individuals,” Bonchek wrote. “Companies grant permission for individuals to access big data, while individuals grant permission to organizations to access little data.”

Returning to the wearable fitness device example, little data would comprise the informational insights that are delivered by the tracking module, including distance traveled, weight changes, calorie intake, etc. A big data initiative related to these findings would require that the consumers utilizing these fitness trackers grant access to this information. From here, an organization could analyze a whole host of little data sources to offer a more global, overarching look at users’ habits.

Leveraging big and little data

If your company is interested in harnessing the power of little data as part of a big data strategy, it’s imperative to have a partner that can help fill in any gaps. Aunalytics has access to a variety of data sets, including those that can provide the right insights for your business.