#5 Implications of the Digital Insight Management decision-making model


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We’ve chosen to present our top 5 key implications of the DIM model to conclude our four-part series exploring various decision-making models. Following on from our in-depth analysis of RAPID and The Lean Analytics Cycle, this list identifies Digital Insight Management fundamentals, focus and implications.

Does Digital Insight Management equal efficiency?

As noted in the introduction to this series, the DIM model balances ownership, action, and accountability to leverage an already existing investment in Digital Analytics and optimization. This leads to dramatically improved and far more efficient delivery of insights. Additionally, the DIM model recognizes common failures within organizations and proposes tips on how to solve each problem. Although it has been widely recognized as an effective decision-making model, and has even been adopted by Sweetspot as our underlying methodology, we would like to share some of these key implications.

#1 Clarity in ownership of data & decisions

The DIM model prioritizes the need to define clear ownership of data and decisions and to achieve a marked performance improvement over time. Individuals should know and understand where their responsibilities lie. They ought to have the authority and knowledge to confidently take ownership of data relevant to them. The idea is that decisions can be attributed to individuals allowing for transparency and improved decision-making over time.

There are a number of issues that may arise when defining ownership of data. For instance, some individuals may be reluctant to take on the responsibility, while others may disagree over who should have it, and there may even be multiple team members who want authority over the same data. Deciding on who should take ownership requires careful consideration of the expertise and experience of those involved, the resources at their disposal, and their authority to carry out decisions. Furthermore, agreement over the ownership must be achieved.

#2 Effective team collaboration

The most effective ideas and insights often come as a result of team brainstorming. The DIM model recognizes the importance of teamwork and collaboration, by asking individuals to work together to make the most of their data analysis. The DIM model supports the belief that employees can work as a team in order to find the most valuable insights which in turn may create solutions for their business.

Unfortunately, effective collaboration is frequently more of an uphill struggle, rather than an easily traveled path. Some people are innately far more productive when working independently on a task and they may need time and support when adapting to a teamwork environment. Their skill of working individually is highly valued on other occasions but the DIM concept supports teamwork as a key factor.

Furthermore, HR theory demonstrates many other challenges to effective collaboration. Some teams may experience a phenomenon called groupthink. The main idea is that people avoid conflict to arrive at a consensus, without the need for critical thinking or alternative viewpoints. If group members feel a certain amount of loyalty or in general are non-confrontational, this will interfere with the ability to properly make decisions in a group setting. Another  impediment to effective team collaboration can be the personal objectives of an individual. Some organizations have cultures where competitiveness is encouraged. In this environment employees naturally look out for their own professional aims and can display issues when adapting to the necessary lines of communication involved in team collaboration.

#3 Analytical understanding

The DIM model emphasizes the evolution and importance analytics plays in the workplace today. As available data grows at an impressive rate, organizations must be mindful of the tremendous significance of successfully converting the results of data analysis into digestible information, thus allowing for more informed decision-making.

Nurturing an analytical culture is crucial for decision-making practices to evolve and respond effectively to data-backed insights. Whilst the construction of this culture is underway, many obstacles need to be surpassed. In some cases, there may be resistance from those who have always relied on their ‘gut feeling’ to adapt to a data based decision-making process. Some organizations face a lack of resources, expertise or even management buy-in – all essential building blocks required for the development of a durable data-backed culture.

#4 Developing, clarifying and clearly setting out requirements from the start

In order to ensure transparency and clear guidelines to follow, the DIM model suggests establishing accurate and organized requirements from the very beginning. Assuming that everything goes to plan, all requirements should be well understood, and followed from the development stage right to carrying out of the final result.

Clearly defining requirements from the beginning provides a good base from which to start, but there is always the possibility of error in even the most well thought out plans. The DIM model needs to recognize that sudden changes may affect how organizations develop and execute decisions. Some reconfiguration of certain parts of the process may be required due to lack of information – resistance, even, from some, to dedicate the necessary amount of time, planning, and follow-through of their plans.

#5 Accountability for decision-making and action-taking

The DIM model suggests using a “Hub and Spoke” model to define roles and accountability throughout the organization. The ‘hub’, or principal analytics team, will need each ‘spoke’ to fulfil their responsibilities. If it runs smoothly, the benefits can help organizations to find valuable insights. If one spoke falls short, this impedes the hub from creating valuable outputs. This can also add pressure to every spoke, or department, working to understand their own analytics. There is an interdependency that is important to everyone involved in the process, since each individual must be responsible and actively participating to allow for the decision-making process to properly take effect.

work flow

Sweetspot’s ‘User Stories’ reinforce the importance of accountability and taking action. The workflow feature is incorporated into the very dashboard, allowing companies to react more quickly to alert notifications and respond more effectively as a consequence.

So…what does all this mean?

Our aim here has been to draw attention to how powerful a decision-making model investing in digital analytics and optimization for business advancement the DIM model can prove to be. At Sweetspot, we’ve had first-hand experience of these enhancements having followed the guidelines set out by Peterson. To arrive at a well-informed, data-based decision, organizations are strongly encouraged to turn these tips into reality.

Flexibility and understanding are fundamental – and constantly evolving. Adaptation is the key to change.

The DIM decision-making model presents various challenges, which can be influenced substantially by human error. Lack of communication and organization are two of of these obstacles. We are not suggesting that the DIM model is an ineffective or impractical decision-making model, but rather highlighting the possible stumbling blocks that may be encountered during the implementation of a fruitful decision-making process.

It would be great if you could share your organization’s utilization or need for the implementation of the DIM model. What are some of the challenges that you have both faced and consequently overcome?

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Holly McKendry

Sweetspot Marketing Director. Wakeboarder & travel enthusiast. Communication Studies graduate of Texas State University, San Marcos.

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