Organizations need automation to access data more efficiently so that decisions can keep up with today’s breakneck speed of business.
To keep up with the pace of business today, leaders must constantly make quick decisions on behalf of their organizations—but if they’re not able to evaluate the full business case before taking action, these decisions can lead to unsatisfying and even harmful outcomes. So how can organizations achieve a quicker time to decision while still ensuring positive results? The answer is automation.
It’s no secret that looking at an organization’s data can reveal where the opportunities for improvement lie, and many companies already leverage business intelligence (BI) and analytics tools to help them make sense of this information. Harvard Business Review recently found that 86% of respondents believe extracting new value and insights from enterprise data is very important, and 75% see it as essential to delivering actionable intelligence to employees enterprise-wide.
Unfortunately, even BI tools don’t always move as fast or as accurately as professionals need for them to be able to operate with agility and stay competitive. Though by reinforcing these tools with automation technology, they can be more efficient and effective. Here are four ways automation helps organizations to unlock their full analytics and BI potential so that they can derive more value from their data to make—and quickly act on—business decisions.
See also: Data: The Competitive Differentiator for Innovation
Securing a 360-degree picture of the business
Effective business decisions can’t be made in a vacuum, which is why decision-makers need to have a clear picture of the state of their business before setting plans into motion. Here’s where legacy systems, which many of today’s enterprises still rely on, sabotage a business’s time to decision and, therefore, time to value. Because these systems often lack APIs, extracting data from them to inform BI and analytics tools is often a time-consuming and involved process, which means decision-makers either omit certain details in their considerations for the sake of speed or wait until data is updated.
Automation technologies like robotic process automation (RPA) can overcome this obstacle by pulling data from multiple systems, such as legacy systems, virtualized environments, and systems that don’t have APIs (e.g., a website), and consolidate it within a centralized location. Automation also can translate data into a language and format the BI and analytics tools understand, cutting down on the need for more analyst intervention. For example, automation can take unstructured data like PDFs, emails, scanned documents, and even images and handwriting and consolidate it into a single data source that is ready for analysis. Not only will this provide users with a more robust picture of their business, but it also enables them to put that information to use sooner.
Enriching data quality
Bad data has the power to sabotage the accuracy of even the best-intended business plans, making data preparation an essential step before analysis. When done manually, though, preparing data can quickly monopolize employees’ bandwidth.
In addition to data collection, analysts can automate data cleansing and repair to significantly reduce how long it takes to prepare this information so that they have more time to actually analyze it. Once RPA quickly extracts data from across systems, these software robots can also check its quality before compiling it into the preferred format for BI software to read and for analysts to review.
In addition to speed, automated data processing eliminates mistakes that can occur through manual data entry, resulting in more accurate and, therefore, informative data. For example, when London’s Brent Council automated its rent change process—a highly manual activity that required employees to copy and paste information to make the rent changes, which inevitably led to processing errors—a single rent change dropped from four minutes to 40 seconds, thereby shortening the time to decision. With automation supporting the preparation process, companies can focus their talents’ bandwidth on activities that require their critical thinking, which fuels innovation that can hasten business momentum further.
Transforming ideas into action
BI tools can lead users towards smarter decisions, but the onus remains on them to set those decisions into motion. Additional steps—even ones as seemingly small as having to leave one application for another—can easily and unfortunately weaken the odds of action being taken.
Automation can help turn information into action. Some newer analytics platforms feature one-click calls to action on their dashboards so that users can immediately act on the insights the platform produces. For example, if an IT service management dashboard reveals discrepancies in a data set, the administrator could automatically deploy a software robot to investigate the incident without them having to leave the dashboard. The robots can also be configured to launch automatically if defined criteria are met within the system.
Likewise, once automation sources data for BI and analytics tools, it can then extract information from those tools’ output (e.g., reports, databases) to inform other IT and business process automations. Whereas extracting data from a BI system would traditionallyrequire either new code or manual extraction, RPA robots can be configured to pull it automatically and then apply it to other activities. A robot could pull IT information stored in reports (such as which employees own or use an IT asset) and leverage it for IT management and maintenance activities.
Aligning teams on BI insights
Teams are able to move quickly when everyone is aligned on where the business stands, but granting everyone access to BI and analytics platforms or continuously sharing reports manually isn’t always feasible or efficient. Organizations can instead democratize BI by using automation to facilitate information sharing. BI and analytics dashboards enhanced with automation technologies can be programmed to distribute summaries of their insights to employees across the organization via preferred channels such as Teams or email, in digestible formats like PDFs and PowerPoint.
Users can determine whether these reports are distributed on a regular frequency (e.g., a daily status update on sales activity) or triggered by a defined event (e.g., when a logistics backlog escalates to a critical level and requires immediate attention). By automating information sharing, organizations keep their analysts as analysts instead of administrators.
Organizations already have the information they need to make smart decisions; they simply need a way to access it more efficiently so that those decisions can keep up with today’s breakneck speed of business. Adopting BI and analytics tools on their own won’t be enough to uncover goldmines of data, though. By coupling these tools with automation capabilities, decision-makers will be able to unlock and action the insights secured through these tools more effectively so that the innovation never has to slow down.
