Enterprises that prioritize agility have been quicker to embrace digital transformation, giving them the ability to swiftly make adjustments to optimize customer experiences. To compete with today’s fast-paced changes, data-driven business agility is the new standard.
Agility as the key to success, and even survivability, is nothing new. Stop to consider the brief moment that Netflix was mailing out DVDs. They seamlessly transitioned to streaming, while Blockbuster seemed like a dinosaur with its feet in shackles made of plastic cases and costly building leases. This is just one example, but the success of businesses has historically been tied up in their ability to shift and make changes quickly to meet customer preferences.
Whether agility needs to be applied to employee working conditions and tools or to anticipating customer preferences, agility is a critical factor in determining a business’s ability to adapt and improve in terms of productivity and performance.
Prioritizing agility comes with risks, and that risk is particularly felt during highly volatile settings, such as during a recession. But when you consider that many business leaders still rely on their gut for major decisions, building data-driven business agility may not sound too risky. In order to make agile decisions that are good for your company, you don’t just need data. You need data that meets three key requirements:
Accuracy: Research developed by Qlik and IDC found that just over half of businesses say they have found and reported at least 70% of the valuable data from their organization, but they report challenges when it comes to processing it.
Successful investments in data capturing and processing demonstrate the ability to improve both productivity and performance, with enterprises reporting that revenue, profit, and efficiency all improved by 17% on average.
Too often, in a rush to embrace data-driven business agility, enterprises don’t question data and make decisions based on inaccurate analysis and insights. Data needs to be correct, complete, and secure in order to be useful for capturing and processing. Enterprises also need to ask whether any particular set of data is really the best data to rely on for specific decision making.
Timeliness: Having the data isn’t enough; it must be relevant and up-to-date for the moment in which the decision is being made. In the past, getting data ready for analytics using an enterprise resource planning (ERP) or customer relationship management (CRM) system might have taken six to nine months. This is no longer the case, so be sure that you are optimizing the timeliness of data before relying on it for business decisions.
Talent: For data to be useful in decision-making, you need to be sure your team has the skills to use it. This means that they also know what to do when the data is incomplete (rather than going with a gut instinct or guessing at the remaining information) as well as how to understand and question data. These aspects are critical for improving operational efficiencies and productivity, as well as staying current on customer trends that may shape your development of new customer experiences.It’s important that businesses prioritize data-driven business agility, rather than relying on luck or instincts to make critical decisions. For more information about readying your data analysis for more informed decision making, contact us at Access Tech.