Data drives everything in the enterprise. But not all data is equal, and not all data is valid.
That means trouble for business efforts that rely on the data accumulated daily to forecast performance, plan strategies, and evaluate success. The variability of data is a byproduct of the applications that generate and store it because each app has it’s own peculiarities and data formats. CIOs rely on master data management tools and techniques to bring the totality of enterprise data together and make it usable and reliable.
Here are the most important drivers CIOs identify as key performance indicators that can be improved by master data management.
Customer nurturing – Consolidated data can be used to review customer purchases, payments, interaction with company representatives, and then combined with external publicly available data. The results can be analyzed to provide insights into the customer’s future plans for expansion and shared with the customer to encourage cooperative planning based on common goals.
The company’s records indicate how their customer orders and their payment history. This provides a good basis for reliable forecasting allowing the sales staff to precisely tailor their offering to include financial incentives, bulk purchase options, or even customized product assortments that best fit the needs of both the enterprise and its customer.
Profitability by segment/customer/product – Revenue and sales reports can generally be produced by most ERP systems. But finding full profitability is different because the calculation should ideally factor in non-inventory aspects that include marketing, customer support, and any number of activities not directly connected with product cost. That’s difficult to do when the data for all the activities is separated across multiple data silos with different formats.
CIOs who understand the breadth of interactions that contribute to product success and ultimately to profitability employ master data management tools and techniques to create full views of the factors that make up profitability.
Grouping different aspects can deliver insights about customer lifetime value (CLV) or product lifetime value because aggregated data can span multiple sources. One example might be to evaluate social media sentiment across multiple platforms over time to understand how changes in product, pricing, and region affect profitability. Those kind of findings can be useful to product designers and marketing as they look to make improvements.
New sales related to activities – Marketing is not the only factor to measure when looking for insights into new sales but it’s the most obvious. Adding data generated by supply chain processes, social media, customer support interactions, and seemingly unrelated activities like supplier compliance with orders can shed light on where, when, and how new product sales are generated.
Those data stores can only be evaluated together after they’ve been cleaned and validated. When considered individually by the different business units that manage each of the separate processes, divergent and even conflicting conclusions can be drawn that can lead to unproductive actions. Only a singular view of the enterprise’s collective operations can produce effective observations.
Inventory movement, location, volume – Product inventory data files are often assumed to be accurate and are the default source of information about company’s products. In reality, unless they are continually managed and combined with other data throughout the enterprise they are often unreliable and can easily lead to erroneous conclusions and subsequent poor business performance. Product information comes from multiple business units including planning, design, purchasing, accounting, marketing, sales, manufacturing, and more.
Each has its own version of costs that go toward the final product and even small issues like whether a product is counted by the individual unit, case quantity, or palette need to be resolved into common ways of calculating unit costs. Deriving a single source of the truth requires all components to be considered and combined in common nomenclature. This needs to be an ongoing process and one that the CIO must lead as new data sources come into play or change over time.
Costs and productivity of services contracted – Service providers offer wide varieties of agreement types depending on the services they provide, and each is likely to include its own SLA that defines what will be provided under what conditions and within specific time frames. Enterprises may have hundreds or even thousands of contracts for service and the differences between them seemingly irreconcilable. It’s critical to include external services in master data management efforts so that costs can be evaluated, compared, and eventually negotiated to the company’s best benefit. The results of normalizing the costs for services should then be incorporated into larger scale evaluations of product pricing with differential burden rates assigned at a granular level. This kind of cost attribution is only possible when data from these service contract has been effectively managed.
Master data management requires the initiative and understanding of CIOs who understand how data can be leveraged to the advantage of their enterprise.
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