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Overcoming Obstacles to Full-Scale Business Intelligence Adoption in 2021

Data analytics isn't just for large organizations anymore. As businesses and community collectives increasingly move their operations into digital spaces, the vast amounts of data being collected pose an opportunity for them to get to know their stakeholders better.

While the security implications of this migration are hard to be taken lightly, the potential for game-changing insights is likewise enormous. Indeed, today's consumers are demanding highly personalized experiences, and this isn't a trend that's going to disappear anytime soon, according to McKinsey and Company.

The right business intelligence platform can help you unlock the power of the data you collect without exposing you to governance pitfalls. However, many organizations face hurdles when adopting a full-scale BI solution. Here are five obstacles that you will face and how to overcome them.

Data Accessibility Limitations

Cross-platform accessibility is the key to delivering value. These days, many self-service BI tools can be accessed from the device of your choice. However, you'll need to work with everyone in your organization's leadership, as well as on the vendor's side, to make sure the solution integrates well with your goals, and without exposing any new vulnerabilities.

What's more, easy access might lead to incorrect ad-hoc solutions being drawn, so make sure you install analytics standards that serve as guidelines for your reporting.

Mike Ferguson has witnessed the analytics industry grow from a nascent one to the exciting one it is today. Despite this rise, companies still have issues accessing the insights they crave. "In a world where data complexity is on the rise, companies have to put in place the foundations for a data-driven enterprise that enables the business to quickly and easily find the data they need, know that they can trust it, and be able to access it quickly to deliver value," notes Ferguson in a think piece about business-ready data.

Poor Data Quality

Your analytics conclusions are only as good as the information that goes into them.

While companies are up to their necks in data, not all of it is relevant or of good quality. Two examples of data-related issues that you'll face are relevant data being deeply buried in your systems and your analytics reports delivering convoluted results that stunt progress.

The solution to these issues is to implement a strong data quality management program. Start by having your business executives define their goals and relate this to the data you collect. Evaluate your data for integrity, uniqueness, validity, accuracy, and consistency. For example, a data point that repeatedly appears in your data sets isn't unique, even if it might be valid.

You'll need to install processes that identify non-unique data points and clean them. Neglect this, and you'll end up creating reports that exaggerate that data point and lead you to draw incorrect conclusions.

Perceived High Costs

Talk to any senior executive about implementing BI, and the first thing you'll hear is that it's probably too expensive. The fact is that many mid-sized businesses look at enterprise analytics infrastructure and think they need the same number of data warehouses, IT infrastructure professionals, an army of data science analysts, and a headache around making sure all of the data pipelines are secure.

This degree of investment was probably necessary during the early days of BI adoption. These days, however, self-service BI platforms have truly democratized data analytics for organizations of all sizes. These tools will help you examine weaknesses in your organization and increase your ROI.

As a result, your investment in these platforms more than justifies itself. The right BI tool for your organization will allow you to gather data from various sources and bring them together into an easily deciphered dashboard.

Some companies increase their costs by choosing tools that don't allow them to connect disparate forms of data. As marketing expert Neil Patel says in his guide to business intelligence adoption, "Make sure you find a business intelligence tool that makes it easy for you to connect with your existing data sources. It's worth noting that not every business intelligence software on the market will integrate with specific databases. So don't make assumptions; always double-check that your data is compatible with the software in question."

An agile and cloud-based BI platform will help you make sense of your data at a price that will almost certainly result in high ROI. The key is to make sure its features suit the nature of your data.

Lack of Organizational Adoption

Most BI projects start as well-intentioned pilots that generate great results but fail to scale to the entire organization. It's a phenomenon Gartner analyst Rita Sallam has witnessed over and over. "Over the past 10 years, with the rise of big data, we've done a great job at storing and managing content, or X data. What we haven't done is a great job at using that pervasively across the organization," she notes.

Your BI program must receive buy-in from key stakeholders throughout your organization before the pilot begins. Your pilot should also measure the right KPIs. Many organizations measure vanity metrics that make them feel good but don't result in any insight. The ultimate aim of a BI program should be to democratize data across your company.

With this in mind, involve both business and technical groups in your pilot project and have them agree on common goals. This exercise will help remove any barriers between the two functions.

Asure your employees that analytics are there to enhance the quality of their work, not replace them. As BI permeates your organization, you'll find that these exercises will help drive a culture of data throughout, and you'll face a low degree of resistance.

Flying Without a Strategy

Defining a clear BI strategy is a challenging task. Most companies think of BI as driving decisions in certain business units.

Tying your BI program's objectives to the most critical results that your company wishes to achieve is a great way to align every aspect of your organization to your BI program. This approach will also help you avoid measuring the wrong KPIs.

As an analytics expert, Chris Penn states, having an overarching data strategy is mission-critical, and the best strategies are those that focus on boosting sales. "When we have to approach data-driven marketing, and data-driven strategy," he writes, "we've got to approach it from the perspective of a sale, not what's best for the company, not what's best for the analytics department or the IT department of the marketing department."

A Necessity in the Information Age

As the amount of data businesses collect increases, your organization must focus on a BI program that is driven by quality. Overcoming the five hurdles you've read about here will help bring your organization onto the right path and allow you to become truly data-driven.

By Evan Morris, Network Security Manager

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