Business intelligence solutions need to accept data from a variety of sources. Image for representative purposes only. Source: Shutterstock

Business intelligence solutions need to accept data from a variety of sources. Image for representative purposes only. Source: Shutterstock

Why business intelligence solutions must not be limited by data sources

DATA is magnificent.

Two of the more popular quotes today “data is the new oil” and “where there is data smoke, there is business fire” highlight the importance of data. Data helps companies make intelligent decisions.

With the internet of things (IoT) turbocharging the ability of businesses to capture data in real-time, more and more businesses believe they’re gaining a competitive edge. Unfortunately, that’s not true.

In practice, although most organizations value business intelligence and invest in tools that help managers build their own inferences, dashboards, and reports, they don’t make use of all the data that is available to them.

To be honest, the state of the business intelligence landscape is better today than it was a couple of years ago. Organizations have broken down the silos that existed between divisions such as marketing, finance, and human resources, to help combine data and paint a more holistic picture of the business.

However, in the absence of external data and access to a partner ecosystem, managers are unable to unleash the full potential of their business intelligence solutions.

Some examples of the external data that can bolster decision-making for managers and business leaders include social media mentions, economic metrics, financial market data, value chain ecosystem data, and so on.

Obstacles to better business intelligence

Truth be told, business intelligence software, especially the self-serve kind, often has the capability to allow for managers and leaders to import additional data in order to augment their reports.

Unfortunately, in practice, this isn’t as easy as it sounds — and for good reason. Here are some of the obstacles to acquiring new data:

# 1 | Accessing the data

Accessing data from new sources is often difficult for managers because they might not be technically-equipped.

Social media data, for example, can often be downloaded via application programming interfaces (APIs), however, most managers might not be able to find the ‘feeds’ and integrate them appropriately into their business intelligence tool.

Further, managers might also have access to data on different intelligence platforms such as Bloomberg, Reuters, etc — which they might be able to access via APIs easily but might not have the right to download it and use it outside of the original platform.

# 2 | Cleaning the data

The most important thing about data when it comes to business intelligence is ensuring that it is mapped clearly in the tool that is combining it with internal data to analyze it appropriately.

When downloading data from social media, for example, the data might not always be simple to clean.

If managers want to understand how many people on Twitter speak positively about the brand every hour, then they need to create and run additional scripts after they download the data to analyze the sentiment, and then use that data inside the tool in conjunction with existing data to gain meaningful insights.

# 3 | Governing and protecting the data

Although there’s a strong emphasis on governing and protecting personal information, the reality is that all data needs to be governed and protected appropriately, especially within an enterprise.

Sometimes, when data is downloaded from protected or pay-per-use sources, organizations have a legal and formal responsibility to protect it from misuse, intentional as well as unintentional.

Further, when external data is used by managers to create business intelligence, the resulting methods and models might often constitute intellectual property and hence, demand additional governance and protection as well.

Creating better business intelligence

Business intelligence in this day and age cannot simply rely on internal data despite the breaking down of silos and better interdepartmental access.

Managers that augmented their organization’s data sets with external data using creativity tend to be able to craft insights that are not only meaningful but also unique.

In the e-commerce industry, for example, IT teams often work with managers to augment business intelligence solutions with data from social media and logistics partners to ensure that items that customers are talking about are always in stock and ready to ship.

Similarly, businesses dealing with fresh produce often tend to augment their business intelligence packages with data from reliable, paid sources to map historical as well as forward-looking environmental and economic data, to be able to make better predictions about sourcing, pricing, and logistics.

In the coming months, organizations that want to gain a competitive edge that leverages on their investments in IoT, AI, and 5G, must think about augmenting their business intelligence platforms and encouraging their managers and leaders to identify external data sources that will be most useful to their analyses.