Digital strategy – Becoming data driven

December 6, 2021

In previous articles, we have discussed a route to take charge of value creating processes and supporting data streams (in contrast to a department/silo approach). As we gain knowledge about the data needs of the various users within the organization, we will also see the need to structure the company's proprietary data (master data) in order to provide it in a meaningful way. 

If done properly, a structured data set increases the value of data, as cross functional reports may quickly be established, and trend analysis of key data points (KPIs) may be tracked. In short, your data can be activated (utilized) and do their part to strengthen your value proposition. 

Most businesses have an established portfolio of software systems, each providing a function to the basic organization (hour registration, mail communication, QMS system etc.) or specialist teams (engineering systems, accounting system, planning software). Typically the data generated by your operation spans multiple of these systems. Thus, structuring your master data means creating logical partitions for your data in the context of your business. Data sets can for example be:

a) competency (by capability type) 
b) work processes (in stages or phases) 
c) production capacity (machines or tools by type) 
d) projects (by archetype) or products/services (by category) 

Master data ownership – data input 1 time

Activation of data across the value chain not only saves time, it also removes a huge source of errors in duplicate values and outdated information. This prompts some very important questions in your organization:

  • Who owns (can change) the data categories we have established above (a-d)?
  • In a distributed system – where are the various master data stored? How is it accessed by other users?

Of course, in a scattered software portfolio it is meaningless to talk of a central data warehouse. That requires extensive data integration, a costly and difficult task for any company. While all parts of this article series represent areas that require strategies and competence that few have, everyone still needs all of it to stay competitive. The software is only one piece of a larger puzzle and the sad fact is that most ERP system implementations fail due to lack of attention to these details as well as the shortcomings of the systems themselves. We will talk about integration extensively in our next article available soon: Assessing the software portfolio (Coming soon)

Activation of data across the value chain is the cornerstone of any digital strategy. Centralized databases are not new, but cloud based technologies open up a new set of possibilities and rules out ways of thinking. Digital mapping of manual processes is also a stepping stone that enables further process and task automation.

Effortless data harvesting

It is the quality of the data that is the foundation for the success of your digital strategy, so selecting the right tools to gather this data is vital. Digital transformation does not only save time, it also removes a huge source of errors and opens up new opportunities for those that can adopt.

But no matter how far your data flow is integrated (or not at all), you need to start thinking structurally about your data, data flows and handovers. This is an important step in your structured approach to process improvement. 

A word of warning here:

This displays both the power and the weakness of digital support systems. If used properly, all information is at your fingertips. If information is jumbled or inaccessible to you when needed, user trust in the system will drop like a stone. Also, if just some users chose to work outside the system (favoring their yellow stickies and excel spreadsheets on their personal drive), transparency and value of remaining data drops close to zero.

It is therefore important that data harvesting tools are made to suit user needs and provide value to each user group at an early stage. This increases the user adoption speed, quality of data and overall value of the digital effort.

Acting (out) on your data

Harvesting the data from your processes serves 2 primary purposes:

  1. To improve the basis on which to take mitigating actions (visualize and address issues)
  2. To improve the flow of work within the process (transparency and bandwidth)

The first point is improving the basis on which managers can evaluate and troubleshoot problems. The data collection does not add value before it results in action. Being able to spot i.e. potential delays and act on it (staffing up, reprioritizing, rescheduling) can mitigate most or all negative effects on the project. The same might be true for productivity or quality issues. Having enough up to date data, increases the birds-eye view of the current status, and ensures adjustments are made sooner and at the correct scale in relation to the problem at hand. 

The second point is addressing the day-to-day effort of the doers. By providing necessary input on time, clearing up priorities of tasks and adding transparency to the progress of others, allows for better productivity and planning of upcoming tasks.

The transition from manual to a data driven way of working may seem straightforward: “if we can provide better tools and information, performance will increase”. But this is not always the case. A project manager with decades of relying on “gut instinct” will not automatically adopt and trust the data driven insight. 

Information carriers – carrying the atoms of business

As you continue your digital journey and process improvement, you will unlock new insight into how you need to structure data in order to provide it to users in an actionable format. The work processes span multiple functions (engineering, procurement, purchasing) each with their own data needs. The initial logic may be to define your tasks, objects or drawings as the vessel to bridge data between the functions, but this is seldom enough to serve all needs, or may result in too much data in the system for the user to sort out. For those interested in diving into some more detail on information carrier objects and granularity, we have created this supporting article: A3.3.1: Information carriers and system granularity.

Do not despair

While the prospect of designing a total data structure for digital support systems from scratch may seem daunting at the outset, fear not, help is available. For most business models there are best practice data structures available. As for process mapping (see Mapping your value creating processes) it will be a huge advantage to tap into these industry “building blocks” when considering data structures. This is where Limber can add value to your situation, and facilitate your digital success at a fraction of the cost and time.

If you liked this article, the next article in the series will be available soon: Digital strategy – Assessing the software portfolio (Coming soon)

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