AI Readiness: Data Management

As we saw in the previous section, operational readiness includes many areas of your business which are not related to IT or data at all. But for today we want to focus on data management. According to oracle,

Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management is to help […] optimize the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization.

That means the goal of data management is to maximize the benefit of the organization. We defined the benefit of the organization as the increase of the voice of customer (VOC), voice of employee (VOE) and voice of business (VOB).

Thus for an operational readiness you need a high-level data management strategy which optimizes the use of data to increase the VOC, VOE and VOB. As you see, this role is a highly strategic role as it combines both business, customer and employee needs together with the data domain. Any grass root approach in trying to optimize data by data source (e.g. single software products or machines) will definitely lead towards non-optimal solutions. Data management needs to be as close as possible to the CEO, if you want to achieve operational readiness.

Data management is a truly cross-sectional function within the business as it touches all areas of the business. It touches cyber security, governance, compliance and data protection. Furthermore it needs to be aligned with the foundational readiness (data infrastructure, cloud strategy) and all internally oriented and externally oriented products and services. Internally oriented services are for example human resource management software, payroll accounting software, IT service management software systems or knowledge management software systems, while externally oriented products and services are customer service management software programs, digital or physical services or products (incl. IoT) or account management and customer relationship management.

If you look at the multitude of applications, dimensions and levels we discussed over the last few days, you might understand why it is very hard for many companies to have a decent, holistic data management. Data management is still a relatively young area for many companies and you can still find many businesses with no data management at all. You often find data silos, incompatible data formats or even no (digital) data at all. In order to tackle all of this at the same time, it is important to change our viewpoint. Instead of trying to develop a holistic data management, we should look to develop a holistic business process management, because data is created out of business processes and each process step creates specific data points. Thus if we can optimize our existing business process management, we can help to create and optimize our data management, which leads automatically to operational readiness.

In the next section we will look into enterprise resource planning (ERP) and ERP software programs which holistically try to manage and integrate a business’ financials, supply chain, operations, reporting, manufacturing, and HR activities.

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