IBM estimates that 2.5 quintillion bytes of data are created each day. Of course, your organization produces just a small fraction of that total amount, but it’s the most important data for your business analysts.
Unfortunately, finding what’s important is a major time drain. Data analysts spend 80 percent of their time manually sorting through data to compile spreadsheets. They’re copying and pasting data from one document to another, and building micros and formulas to process complex numbers. Most of this brute force work is time-consuming, expensive, and inefficient; manually preparing, cleaning, and consolidating data from different sources is a near-impossible task, not to mention the potential for error or bias.
Clean, accessible data is no longer a “nice to have,” but a critical business asset that drives compliance in the health care field, for example. The more data an organization collects from disparate systems and applications, the more complicated it becomes to process into clean, easily digestible spreadsheets. All organizations face data challenges, but enterprise organizations in particular are dealing with terabytes of disparate data from a variety of sources, both internal and external.
But what is clean data? For one, it’s readable – there are no null values or missing data sets. Two, it’s complete, so every part of the data is visible. In other words, if a retailer with 500 stores is looking at the data, the CEO can see the total revenue and each store’s individual revenue.
While it sounds like a white whale, be assured that clean data is achievable. There are emerging solutions that can automate the data-gathering process. These tools pump out clean data that’s easily digestible and provides complete insight into an organization’s inner workings and partner performance, leaving data analysts more time to interpret, socialize, and act on the information.
To avoid the inefficiencies that clutter up the path to valuable data insights, organizations should choose a data preparation solution that meets these five criteria.
- Makes you as self-sufficient as possible. Data preparation tools aim to save time by automating routine tasks. Once an analyst extracts and maps data collection the first time, the program should be able to automate the process for the future. This produces data that’s reusable, readable, and can be executed automatically on an ongoing basis. That leaves data analysts in complete control of their daily to-do list.
- Helps you avoid recreating the wheel. Once data is clean, a solution should be able to easily import it to common spreadsheet or visualization applications, like Excel, IBM, Qlik, and Tableau. These automated data preparation routines can easily be shared with and discovered by other employees. Even better is if these processes can be established as rules, so data analysts can schedule them to collect data on a regular timetable.
- Serves up the right data to the right user. Not everyone is looking for the same information: People in different roles or departments need access to different data. You need a system that allows analysts to create prepared data sets and segmented data for individual users, as well as for specific roles or groups. So while an organization’s CFO will demand financials, the heads of HR might be looking at recruiting insights — sets that are appropriate and relevant to each department.
- Updates data, wherever it lives. Prepared data sets should exist beyond where they were created. The ability to deliver those data sets to other systems, like data warehouses or departmental data marts, is the key to accurate, clean data across the board.
- Always offers access to current data. The program should recognize new data sources so it can automatically classify these new sets and pull relevant figures from them. For example, if you use data from customer invoices, you need a system that can “listen across the network” for any new invoices, whether they reside in a content management system, a shared directory, or even an email inbox. By automatically finding and mining these documents for data, the program promises analysts will be equipped with the most up-to-date information.
Spring is here, so get clean data
Because business-critical decisions are made every day based on data, it needs to be accurate and clean. That means data sets or visualizations must represent up-to-date figures, and must be easily understood, yet thorough. In other words, the analysts should have some insight into how it was prepared.
An automated solution will allow you to do all of this with just a few clicks. These tools free up business analysts to accomplish other tasks, allowing them to better understand their data and present more informed analysis to the C-suite and other business leaders.
About the author
Dan Potter is Chief Marketing Officer at Datawatch Corporation, and is responsible for the company’s worldwide marketing communications, product marketing, and go-to-market strategies. Prior to Datawatch, Dan led the product marketing and go-to-market strategy for IBM’s personal and workgroup analytics products and the online community and social media strategy for IBM’s AnalyticsZone.com initiative. He has also held senior roles at Oracle, Progress, and Attunity.