Business Problems to Analytics Use Cases: How?

business problems to analytics use cases - Decisions - actions - output

Data is the new oil. It is the lifeblood of modern businesses. It can be used to take informed decisions, measure performance and gain insights into customer behavior. But how do we go from a business problem to an analytics use case? How do we unlock the power of analytics to drive real results? Let’s explore how to make it happen.

1. Identifying Business Objectives – Strategic or Tactical

Identifying business objectives (Strategic or Tactical) is an essential part of identifying analytical use cases. When organizations start working on their analytics projects, they need to first define the objectives they want to achieve through their data analysis. There are two main types of business objectives that can be identified: strategic and tactical.

business objectives - strategic vs tactical

Strategic objectives are long-term goals that provide a vision for how the company wants to achieve success in its given field over the course of several years. These goals can provide direction and guide businesses in setting short-term / tactical goals, as well as take decisions regarding investments and resource allocation in order to reach those larger objectives. Examples of strategic objectives include increasing sales revenue, reducing costs or improving profitability, or expanding into new markets.

Tactical objectives are shorter-term goals that outline how a company should go about achieving their strategic objectives on a day-to-day basis. They often involve specific tasks like tracking customer behavior, developing marketing campaigns, or testing new products and services. Tactical objectives help ensure that businesses stay focused on their larger ambitions / strategic objective even when dealing with more immediate problems like customer service issues or product improvements.

By understanding both strategic and tactical business objectives, businesses can better identify analytical use cases for their data analysis projects. Independent analytics projects can be aligned to the tactical objectives which end up helping achieve strategic objective. Knowing what kind of outcomes they want from the project helps companies narrow down which metrics should be tracked and which areas of data should be looked at first for potential insights that can benefit the organization. This way, businesses can be sure that any data analysis they carry out is helping them take steps towards reaching their overarching visions and long-term success in their chosen field instead of just providing them with random bits of information with little practical value.

One of the key aspect of identifying business objectives is identifying the value metrics / key performance indictors (KPIs) which would help measure the progress and success of initiatives taken towards meeting those business objectives. Some of these metrics / KPIs can be termed as leading KPIs and others can be termed as lagging KPIs. Read one of my related post on KPIs – Leading & Lagging KPIs: Concepts & Examples 

To summarize, the first step includes identifying the following:

  • What’ the business objective? Whether its strategic or tactical?
  • What are the KPIs / value metrics which will be used for measuring the performance of initiatives / projects?

2. Identify Key Business Stakeholders (Impacted or Take Decisions)

Identifying key business stakeholders is an essential step in the process of determining the appropriate analytical use cases for any organization. Knowing the stakeholders would help in identifying challenges faced by them (or potential actions they would be taking) and key decisions required to be made by them to take those actions in relation to the business objectives identified in previous step. This would help enable the tailoring of the analytics use cases to meet their objectives and drive desired outcomes. With this knowledge, organizations can better create an environment where data-driven insights are easily understood and implemented. In design thinking paradigm, this is also termed as identifying personas.

It should be noted that the key stakeholders include the following:

  • Ones who would be taking decisions and driving actions towards attaining business objectives
  • Ones who would be impacted and they can be internal or external customers 

The picture below represents the need for identifying the stakeholders early in the game as it would help us identify decisions and later analytical use cases.

business objectives - stakeholders - decisions - 2

Having a clear understanding of which stakeholders need to make what kind of decisions in accordance with their challenges or actions to be taken would determine what type of insights would be required  and how it will be used. This would in turn help define the use cases while aligning them with overall organizational goals. This would promote more effective decision making and help setting up data-driven culture. Accurately identifying key stakeholders also allows companies to ensure that they are leveraging their existing customer base (internal or external) when building out their analytics models, as well as proactively addressing potential customer needs before they arise.

3. Identify Key Decisions / Actions / Output / KPIs (Hypothesis)

Identifying key decisions, related actions and output is essential for accurately recognizing analytical use cases. These decisions are required to be made by the stakeholders identified in the previous step. Decisions can also be termed as levers. Decisions will result actions-output pairs. Actions taken as a result of a particular decision can result in multiple different outputs / consequences which can impact another set of stakeholders (customers). Actions can also be termed as levers. Each of this output / consequences would need to be measured using KPIs. These KPIs can also be termed as leading KPIs. And, each of the output would need to be mapped to final outcome and the KPIs (termed as value metrics or lagging KPIs). The following picture represents the relationship between decisions – actions – output – KPIs.

business problems to analytics use cases - Decisions - actions - output

The set of decision – actions – output can also be termed as the hypothesis as there is a level of uncertainty whether the decision and resulting actions will result in desired output which would help in achieving the desired outcome. One or more hypothesis  would need to be tested while deploying the solution in production as part of experimental design approach. Stakeholders or key decision makers come together to agree upon a set of decisions. Out of multiple action-output pair, one would require to decide upon selected few actions.  

Analytics use cases can be used to uncover valuable insights and answer important questions such as “how did this particular decision lead us to this outcome?” or “what are the most effective ways of achieving our goals?”. They also allow us to identify potential bottlenecks in our systems, optimize processes and increase efficiency. With a clear understanding of key decisions, related actions and their outputs, it becomes much easier for organizations to gain valuable insight into their operations and make better informed decisions.   

Having a thorough understanding of all the relevant components such as decisions, actions, output and KPIs is beneficial for identifying most appropriate analytics use cases. With all the necessary pieces in place, organizations can more accurately measure different initiatives and decisions associated with them. 

4. Identify Use Cases & KPIs / Metrics

One or more similar decisions, actions and output can be grouped together to form different analytical use cases. The idea is to aggregate decisions into a cluster of decisions around a common subject area that have measurable financial impact. This cluster of decisions can be labelled as the use case. We, thus, arrive to so-called analytical use cases. Each of these analytical use cases would be tied to the business objectives just to make sure that we are always aligned with so-what when we are working on each of these use cases. 

5. Prioritize Use Cases

Prioritizing use cases is essential for achieving desired outcomes with analytical use cases. Doing so helps organizations align their strategy with the capabilities of their available resources, as well as to measure the relative impact and feasibility of each case against their objectives. Through proper prioritization, organizations can better understand which use cases will provide the most value to their business in the short-term, while also being mindful of the long-term implications and benefits that each case may have.

The Prioritization Matrix process provides an effective framework to help ensure that stakeholders are able to collectively make informed decisions about which use cases should be given priority over others. This process enables teams to systematically evaluate each use case objectively and make reasoned decisions based on a variety of criteria, such as value to the business, level of risk involved, timelines for implementation, and resource requirements. The matrix then helps prioritize those use cases based on those criteria, highlighting which ones should be given more emphasis or focus compared to others. The following is a sample prioritization matrix with letters representing the use case.

prioritization matrix - business value vs implementation feasibility

The following are some of key points about the prioritization matrix:

  • The prioritization matrix weighs the “value” (financial, customer, operational, and environmental) of different use case against the implementation feasibility (data, architecture, technology, skills, timeframe, and management support) of those same use cases over the next 12 to 18 months.
  • The prioritization matrix process gives a fair playground for discussions on the value of the use case  against its implementation feasibility.

Using a prioritization matrix also helps ensure that no one stakeholder can unilaterally influence decisions regarding which use cases should be given priority; instead it allows all stakeholders to work together and reach consensus through an objective assessment of each use case’s relative merits. This helps foster alignment between different stakeholders and build confidence in the decisions being made by giving everyone a say in how resources are allocated towards certain projects or initiatives.

6. Identify / Create Analytical Solution Design

Identifying analytical solutions is extremely important for analytical use cases. Knowing what analytics are needed to support and answer questions related to the use case helps guide decision-makers in the right direction. It can also help them better understand the data, spot trends, and draw insights from it. Descriptive analytics provide a snapshot of what is happening in a business at present, predictive analytics provide insight into what might happen in future, and prescriptive analytics recommend best courses of action based on current data and trends.

Using these three types of analytics allows businesses to gain a better understanding of their decisions and their impact. Descriptive analytics help to track the KPIs related to actions taken and in turn take different decisions if required. Predictive analytics focus on using historical data to make predictions in case there is uncertainty about the action to be taken. The related KPIs can be tracked on dashboard to validate the effectiveness of the decisions being made based on the predictions and whether one continues to rely on the predictions or choose different predictive models. Prescriptive analytics would result in optimizing the outcome made by the decisions.

7. Identify data sources

Identifying data sources is a critical component of any analytical use case. By understanding the data points available, an organization can better understand the trends and patterns that are driving their business decisions. By using insights from data, businesses can make informed decisions to maximize profitability and minimize risk. Data sources play a fundamental role in all form of analytics use cases including those related to descriptive, predictive and prescriptive analytics.

For every use case, you would need to ask the following question to avoid data bias.

What data do we have (residing in the internal data sources) and what data would we need to collect from external resources to extract the desired insights?

8. Identify Technology & Related Architecture

It is important to identify the right technology and solution architecture for implementing analytical use cases. And, this would require solution and technology architecture for end-to-end solution including system serving predictions, MLOps, feature generation system, etc. Proper selection of the technology stack, platform architecture, and data solution design can be critical for successful implementation and deployment of use cases. 

When implementing and deploying predictive models in production, it is important to carefully design the technology and solution architecture. This architecture should be designed to support data collection and feature engineering in order to build and deploy high-performance models that can stay up-to-date with changing conditions. It should also be capable of handling large volumes of data while providing real-time performance when needed. The technology architecture must ensure that any predictive models deployed in a production environment are secure, reliable, and monitorable so that they can continually feed accurate insights back into the business decision making process. All of these factors contribute to a successful implementation and deployment of predictive models in production.

Dashboards are an essential tool for visualizing data and providing business insights. When designing the technology and solution architecture for implementing and deploying dashboards, it is important to consider factors such as flexibility, scalability, and security. Flexibility is essential for ensuring that users of different roles can access the right data at the right time. It is also important to account for scalability so that more data can be handled when needed. Finally, a secure architecture must be implemented in order to ensure that all user data is protected from malicious threats.

9. Implement Analytical Solutions

The implementation of analytical use cases is an important factor in ensuring that these use cases yield the desired results. All analytical projects are built on the same foundation, consisting of data integration, data modeling, and analytics. Data integration involves connecting different sources of data into a single source for analysis. This could include extracting data from multiple databases, flat files, and other sources. The data must be cleansed and standardized to ensure accuracy and integrity during the subsequent phases. Data modeling involves transforming raw data into meaningful formats that can be used to analyze trends or predict future outcomes. Modeling is typically done using statistical methods such as linear regression, logistic regression, decision trees, neural networks, and more. Finally, analytics refers to the process of discovering patterns and relationships in the data collected through predictive models or descriptive techniques such as clustering or segmentation.

Analytical use cases require careful consideration when designing and implementing their solutions. It is important to carefully identify all relevant stakeholders involved in the process in order to understand both their needs as well as any areas where there may be potential conflicts in how they expect their data to be used. Additionally, it is important to understand all aspects related to security including access control mechanisms for who will have access to which parts of the system and what level of authorization will be required for each user type.

Once a plan has been established for how the analytical use case should work, it is then time for implementation. This stage involves setting up appropriate software solutions and hardware infrastructure necessary for gathering and storing relevant information from various sources along with developing algorithms that can perform desired analysis on this collected information. Business intelligence tools such as Tableau or QlikView can also be employed here if needed depending on project requirements. Additionally, machine learning methods may need to be applied depending on what type of analysis needs to be performed on gathered information and whether it requires predictions or not (e.g., supervised vs unsupervised learning).

10. Create Experimental Design & Deploy 

Experimental design is a popular method of deploying analytical solutions that can maximize the effectiveness of data-driven decisions. This approach is often characterized by multiple trials or experiments conducted in order to identify the most effective combination of factors, inputs, and variables within a system. In this way, experimental design can be used to test the most efficient methods for implementing new or existing analytical solutions.

The primary benefit of experimental design allows decision makers to deploy solutions based on what-if scenarios representing different experiments and gathering evidences to decide on which solution should be considered for final deployments. At its core, experimental design seeks to balance two opposing forces – exploration and exploitation. Exploitation involves working with existing systems and making slight adjustments in order to increase efficiency; while exploration consists in introducing completely new elements that could potentially bring new opportunities. With analytical solutions deployment, both kind of techniques such as exploration and exploitation would need to be followed. While existing solutions would need to be tweaked (re-training models with new data), new solutions (new models trained) would also need to be developed and deployed from time to time.

Conclusion

In conclusion, these are the steps to take when you want to go from business problem to analytics use cases. First, you need to identify your business objectives and stakeholders. Then, identify the decisions, actions, and outcomes that can be impacted with insights. After that, identify different types of use cases and prioritize them according to value. After that, it’s time to start thinking about technology and architecture solutions. Finally, implement your use cases one by one and deploy them into production. If this process seems overwhelming or you would like more information on any of these steps, please reach out via the contact form on my website or leave a comment below. I’m always happy to help where I can!

 

 

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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