Key factors for building a tool for data and knowledge management in industrial services – An SLN collaborative project

The vast majority of organizations recognize that managing Knowledge and Data is a key source of competitive advantage and a basic capability. Yet how many equip their team members with the skills and understanding to effectively integrate the management of both data and knowledge into their operating processes?

Within the Service Leaders Network, we recently ran a collaboration project with a small number of Service Leaders to look at this challenge. The result has been the development of a pragmatic framework and self-assessment tool, that all service professionals can apply in their day-to-day working environment.  A simple management blueprint that encourages managers to ask incisive questions that will increase the likelihood of success of their Data or Knowledge projects.

Before delving deeper into the framework, it is worth understanding how the conversation came about. When we asked a group of service leaders about their Knowledge and Data challenges, they came up with a long list of topics many of which you will recognize. The most popular topics were access to expert product knowledge, sharing specialist competencies, knowledge retention, competency management, knowledge transfer…..the list was long. Such was the interest we decided to form a small team of motivated professionals, from different industries to collaborate and develop solutions that could help them.

Initially, the team’s focus was to share best practices across their businesses, but as the project developed, they realized they needed a framework to judge what was good practice across different solutions and approaches.  We recognized that most managers understand WHY knowledge and data are important to them and they know WHAT they need (hence the long list). But where there is a big hole is HOW to get there.  Through a slow process of virtual meetings, one-on-one interviews (this was the time of COVID-19) and supporting analysis, we moved towards the framework you can see. A simple tool developed by managers, for managers that helps them take actions that will increase the likelihood of success for their data or knowledge solutions. As this was developed as a Service Leaders Network (SLN) collaboration project, we would like to share it with our service management peers.

Framework Overview

Based on experience, the group identified 4 interdependent factors that should be considered when integrating a data or knowledge solution into an organization’s processes:

  • Purpose
  • Data Architecture
  • Process and Tools
  • People

For a business process to leverage data and knowledge to the full, all 4 factors should be considered and where necessary planned for. This is especially important where investment is made in specialist tools and technologies such as Service Management Software, Human Resource IT solutions, and Advanced Analytics Data Solutions. Let’s look at these four areas in a little more detail:

– Purpose

This is the “Why” of the data solution and can be articulated in different ways depending on where the project lies on the Strategy – Operations continuum. The purpose of the data solution should contain some, but not necessarily all the following components:

  • Fit with the vision and strategy of the company
  • The KPI’s or performance measures to be influenced
  • The risk to be managed
  • The value created, costs reduced or loyalty fostered

Without a well-defined purpose, the project is likely to lack direction and so disappoint or fail in its return-on-investment objectives.

Common mistakes: Companies investing in SharePoint as a tool to ‘share data in the business’, without understanding the KPIs that need to be influenced(e.g. customer loyalty) or the data being collected.

– Data Architecture

With a clear understanding of purpose, it is possible to define the data/knowledge to be collected by the process, or the data/knowledge required to support the process. Knowing whether this data is structured (numbers) or unstructured (text/words) is key to defining how it is collected and analyzed within the business process.

Common mistakes: Defining Key Performance Metrics indicators without understanding if the data can be collected and analyzed in a sustainable fashion.

 – Process & Tools

The next component is to define how data/knowledge fit into business processes and the tools required to ensure it is presented in such a way that decisions can be made. Often managers will jump to this step without understanding Purpose or Data Architecture resulting in sub-optimal data/knowledge solutions.

Common mistakes: Remote Data Capture is a common data solution, but it does need to be built into the service process if it is to deliver sustainable value. Too often it is seen as just another activity we do.

– People

Without people’s willingness to engage in the knowledge management process, initiatives will fail.  The key is to design this factor into the Knowledge/Data Project from the start, whether that is building a culture where knowledge is shared, developing the skills required to support the process, or simply good old-fashioned change management to ensure engagement. This is the component that many business leaders miss when implementing knowledge management solutions.

Common mistakes: Within the Service CRM processes, users do not update master-data, or worse still, simply bypass specific data entry requirements to save time, as they do not understand the implications of their actions.

 Self-help tool to enable success

A framework is all well and good as a backdrop, but it is necessary to turn that know-how into actions. We developed a simple self-assessment tool, with the goal of facilitating managers to ask the right questions. By probing the four key factors, looking for the deliverables and actions that one would expect to be in place, we hope that they will be able to identify the weaknesses in the data solution and thereby take corrective action.

PURPOSE: Do you have a clear purpose for the Data or Knowledge solution which you can articulate to both senior management, the users of the program as well as key internal customers?

Articulation of purpose means that it must be written down in a format that can be shared within the organization. Purpose is when the ‘Why’ for the Data/Kowledge solution is clearly stated to a level where it follows what are often known as the SMART guidelines. In other words goals and objectives which are Specific, Measurable, Achievable, Relevant and Time-bound.

Evidence of ‘articulated purpose’ can be demonstrated in a number of ways:

  • Documents or presentations that might be linked to the presentation and communication of strategy, risk, or performance
  • A Business case or business plan
  • The descriptions for company performance indicators. Best practice is for these to be documented not only in terms of their definitions but how they are measured and actioned in the organization.
  • A clear description of a Data Hypothesis (used by data scientists to define the problem to solve) which clearly outlines:
    • The organizational performance measures to be influenced
    • The parameters that influence those performance measures
    • The data to be collected and the analysis to be done
    • The expected outcomes of the analysis and how they contribute to influencing the performance measure we want to impact.

This lack of purpose when developing data solutions is extremely common in many organizations. It highlights a weakness in problem-solving, in particular an understanding of how to accurately define the problem to be solved.

DATA ARCHITECTURE: Do you understand your data architecture well enough to be able to define the Data Tools (IT, Analytics, etc) and integrate them into existing processes?

Data Architecture is generally defined as the models, policies, rules, or standards that govern which data is collected, how it is stored, arranged, integrated, and put to use in data systems and organizations.

Understanding this will influence the tools and business processes required to collect, store and analyze data.

From the perspective of a business professional (as opposed to a Data Scientist) it is probably enough to understand the following 3 aspects of your data:

  1. Is the Data Structured: This is when data adheres to a pre-determined model. Normally numbers or text within a table. This type of data is easier for traditional IT tools to measure, aggregate and analyse. For example, the data          found in an ERP is structure
  2. Unstructured Data: All other data! Until recently this has been much harder to analyse using IT tools. Typically, unstructured data is video, text or sound. In days gone by, researchers would turn unstructured data from interviews and recordings into structured data by laboriously analysing and categorising words, phrases or images. With increased computing power and the used of advanced analytics (e.g. machine learning), this process can be automated using text, voice or visual recognition tools.
  3. Do you understand the 4V’s of your data? The 4 V’s is a common methodology that describes your data: 
    • Volume: The volume of data will impact how you collect, analyze and store data. Big data is defined by the sheer volume, which in turn is evolving with technology.
    • Variety: The different types of data to be used will impact the proposed data solution, in particular, what is structured or unstructured.
    • Veracity: the trustworthiness of the data. Is the data representative and even accurate? This impacts the amount of pre-processing or data cleaning that is required
    • Velocity: is the frequency of incoming data that needs to be processed. This has a significant impact on processes, resources, and technology.

 The 4V’s are only really important when managers are looking at complex data solutions that involve large quantities of (big) data such as found in remote data capture.

PROCESS & TOOLS: Have you integrated the solution into existing business and operational processes with tools that support the users?

There are three basic sub-questions that you should be able to answer if you are to be successful in the execution of your data/knowledge solution:

  1. Has the data collection and analysis process been incorporated into your documented business processes?
    If it is adhoc then it is unlikely that the data solution will be sustainable. A good example is KPIs. When a KPI is an inherent part of the company reporting system, it will be regularly communicated. Maybe contrast this to your own experience of creating a new KPI and your having the pain of sustaining the data collection and sharing of that measure.
  2. Can the tools selected for your data solution analyze the data structures you have identified?
    For example, if you are looking to analyze text in service reports (unstructured data) and you have not planned to use an advanced analytics solution, then it will be a manual process. This will have a significant impact on the depth and scope of the project you are doing or allow you to identify capability shortfalls.
  3. Is the organization designed to support the data solution you propose in terms of organizational structure and technical competence?
    Processes are executed by competent people with the right tools in the right place with the right focus. A good example we are experiencing at the moment in the service world is the explosion in the use of Remote. As the percentage of calls managed using this technology increases, so organizations start to have dedicated teams/people, that require a different way of working. To cope with the shift from Field to Remote support there might be significant changes in skills and capabilities required. Challenges like this are critical to the successful implementation of a data solution.

 PEOPLE: Do your people have the right mindset, values, and skills to take full advantage of the Data/Knowledge solution?

And finally, it is people who operate processes no matter how automated they become. Hence it is important to have built-in a communication and training plan targeted at team members who are part of the program. Key is to ensure that all your people:

  1. Understand the purpose of the program
  2. Have the skills and capabilities to get maximum value from the program

 This factor applies to all people associated with the program from senior managers to individual operation people and even customers.

To give you a feel of how the assessment works, we have put together this very short and light version that you can use to engage with your team and start the ball rolling on your data & knowledge management challenges. Access the assessment with this link https://si2partners.outgrow.us/successful_data_solution

Next steps to take

If you want to know more about this self-assessment approach, then you can contact nick.frank@si2partners.com. We can support you with engaging workshops that will help you and your team identify how to integrate data into your business processes. We also have run a series of workshops that help service professionals to become more data-savvy. To date, more than 200 professionals have participated in these programs which aim to raise the bar in terms of how to use data.