Put Your Data to Work
Every day within organizations across the globe, there are small hiccups and inefficiencies that continue to go undiscovered for years, drain funding and impede business processes. Oftentimes, redundancies and reduced productivity result from poor data quality and data management. In almost every business, there is invaluable data that can provide the necessary perspective to streamline operations and resolve waste.
By considering the logistics, tactics and benefits that can result from intelligent usage of data, organizations can trim the fat, turn operational overhead into a profit engine and become the envy of their competitors. It is crucial to remember, however, that without high quality, cleansed and managed data, it is impossible to incorporate data driven processes in your organization.
Here are a few considerations for how and where data can be your ally and not your enemy.
1. It Starts With Managing Your Data
Similar to the Rime of the Ancient Mariner, "Water, water, everywhere, and not a drop to drink," most organizations are dealing with a deluge of raw data, but lack a strategy for purifying and using it for insight. One major issue with data involves unstructured data stemming from machines.
Machine-generated data can provide insight regarding metrics such as average number of defects during a shift, tracking of late shipments, as well as other quality metrics. Because a lot of machine-generated data is generally unreadable, it requires rigorous processes to make it legible and ready for human analysis. Once data is cleansed, an enterprise can conduct accurate analysis to gain a clear perspective of operations.
Consider a logistics company and the machine-generated data involved with tracking thousands of packages and hundreds of delivery vehicles. The GPS data from your truck fleet, combined with data produced by your warehouse management system, can serve to conduct analytics and more accurately assess delivery ETAs, facility outputs and projected customer complaints. Or the data can be inaccurate and result in faulty assessments, wasteful redundancies and incorrect projections.
2. Insight Driven Decision Making
Prior to putting your data to work, you need to ensure the processes involved with cleansing such data are in place. Data can stem from everywhere: customer interactions, purchase history, warehouse management systems and mobile apps, to name a few. Effective management of data is a vital necessity for organizations interested in accurate insight regarding vital metrics.
Not only is real-time data valuable for insight, it is also useful to analyze historical data to provide additional context. Incorporating historical and real-time data can help conduct predictive analytics by assessing past performance to current initiatives. Once data quality is attended to, companies can leverage bespoke solutions for assessing data and representing it via interactive visualizations, dashboards and business intelligence applications.
Custom software solutions currently available allow instant access to real-time data regarding sales, labor costs and other financial metrics that provide users with an arsenal for better forecasting and analysis. JC Penney, for instance, could have used such tools to avoid the drastic loss in customers resulting from their in-store redesigns and changes to discounts and sales promotions. In an age defined by differentiation and innovation, organizations should be careful with drastic changes that are not kept in check by sound data-validated insights.
3. Data Driven Processes Across the Organization
Without processes for ensuring the quality and accuracy of such data, analytics are essentially useless. Acclimating department heads and decision makers with such data practices is made easy by showing them practical applications of data driven processes. The amount of data stemming from customer interactions with a brand's website, internal enterprise mobile apps and e-commerce transactions, can provide precise perspective regarding operations.
Consider the possible uses for insight within the HR department. What metrics do the top sales people have in common? What performance metrics are strongly correlated with a successful store manager or field representative? Organizations can even identify which channel led to leads or sales with marketing attribution. Accurate attribution is only possible through the analysis of the data trail stemming from customer interactions with ad campaigns, competitor websites and other touch-points.
From optimizing hiring practices to personalizing web experiences, data can provide deep insight regarding employee performance, brand notoriety, security vulnerabilities and more. While data can be an asset, without accurate management data can become a costly impediment to business processes. For instance, the FDA process for naming pharmaceuticals depends on data matching to assess the phonetic similarities between different drugs, to avoid name confusions. If the data matching processes fail, people can mistake their prescription for a similar sounding drug, potentially resulting in serious illness.
Without attention to data quality and data cleansing, analytics and data driven processes are severely hindered. And vice versa, good data without the processes to capitalize on findings make it difficult to discover new efficiencies. Any management team can make the decision to prioritize data management within their organization. In order to instill data driven processes across your organization, data must be managed, cleansed and integrated effectively.