Separating Hype from Reality in the Modern Data Landscape
There are plenty of buzzwords and calculations thrown around in the data atmosphere that attempt to illustrate how the use of data can increase revenue, improve customer experiences and accelerate decision making. While some of the language used to describe data, initiatives can come off as hollow and abstract, a buzz exists because real value can be had.
Organizations can use business intelligence (BI) to transform data into actionable insights, recognize how bad data can hurt revenue and separate cluttered data from metrics that actually provide business value. Among other facets of data analytics, these three points can help an organization see through the buzz and put the information they’re collecting to good use.
According to CIO, BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business. BI offers a way for people to examine data to understand trends and derive insights.
BI technologies and platforms should consist of more than just a number-crunching machine that spits out standard reports. They need to be customizable and user-friendly for the entire organization to use, not just tech-inclined employees. The BI tools can be selected or customized based on what the company is trying to accomplish with their data, and information presented through those tools should be easy to digest and share. While data analytics are mostly being used to help inform future developments, the data generated through BI technologies stays relevant to companies because there’s always a need to look back on past data (and decisions) to evaluate how the company is performing.
All Data Is Good Data
Data analytics are supposed to help simplify processes and help generate more profit, so how can it possibly work in the reverse order? It’s quite simple actually. Data is only as good as the given information. If inaccurate information is run through an analytic tool, the reports generated won’t be reliable, meaning more money being spent to make the wrong decision. Considering bad data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year, practicing proper data hygiene isn’t a small issue.
Data analytic usage on company front lines is still relatively new in the business world, and a lot of companies do not have proper training or an understanding of exactly how their input effects the results of the data tools. If data has to be transported or migrated before analysis, it is possible that a vague rule or poor migration strategy can result in completely compromised data. Organizations should have data professionals validate data after a migration before using the data for analysis or else bad data will alter the results, which will cost the company time and money.
There’s Way Too Much Data
There is so much data available to companies that it is tough to comprehend how much truly is out there. According to one study, most companies analyze only 12 percent of the data they have. That means that 88 percent of data is going unused! With so much information, it’s hard for companies to decipher which information is valuable and what information only muddles the process. The amount of data is only expected to rise because, as the study notes, 90 percent of the data in the world today has been created in the last two years alone.
With so much data available and so little being used, it is important for organizations to comb through their information to confirm what is valuable and what is not. Generating reports or data that won’t end up being used for anything are essentially wasted efforts. Focusing only on data that aligns with company goals will reduce wasted time and speed up decision-making, ultimately leading to improved employee morale.
There are data analysts out there that will portray big data as the magic pill that will solve all of your company’s problems. While data can highlight issues, detect trends and help an organization to make more informed decisions, it doesn’t happen automatically. Make sure your data is hygienic, that you’re only focusing on info that relates to business goals and that you use a user-friendly platform to scale data analysis across your organization. Doing so will make the realities of data apparent, and distance the unneeded hype.