2.1.4 Define Future State

Defining the future state creates a vision of the desired outcome of the change. Defining success for a business data analytics initiative is as important as any other change initiative. Defining the future state includes ensuring:

• the future state is clearly defined and understandable,
• that it is achievable with the resources available,
• that key stakeholders have a shared vision, developed by consensus of the outcome being sought, and
• measurable objectives are established to ensure the desired vision is met.

According to A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide) version 3, purposeful change includes a definition of success.

To establish measurable objectives, analysts facilitate discussions between stakeholders to determine the types of metrics to consider. Working collaboratively, decision-makers select the most appropriate measures to assess using business data analytics. These measures may be a combination of strategic and operational key performance indicators (KPIs). Some KPIs may focus on assessing performance for a specific geography or a target audience. There may be industry-specific metrics such as average revenue per user (ARPU), which is used in telecom, or “store footfall” which is used in retail to count customers visiting the store.

Another important aspect of defining the future state is establishing the scope for the analytics effort. Establishing the scope involves understanding which areas of the organization are participating in the analytics effort and determining what stakeholders have questions to raise and information to provide.

A future state, concerning an analytics initiative, could also include setting a vision about the length and breadth of analytics capabilities. For example, tracking more KPIs, increasing the frequency of reports being generated from monthly to daily/weekly, automating reporting functionality, or having data available in real-time. Apart from descriptive objectives like tracking KPIs on the past data, predictive and prescriptive analytics may involve certain anticipated changes to business processes that drive multiple change initiatives.

The future state of an analytics initiative evolves throughout the life cycle of the engagement. Analysts manage and record the changes to future state.

Given the potential evolution of the vision, analysts are challenged in describing the changes reflected in the current understanding of the future state. Like most other activities in an analytics initiative, defining the future state is continuous and iterative.

The desired output from defining the future state is a clear understanding of the business objectives and the value the business is seeking to obtain from the analytics effort.

Analysts use metrics, KPIs, and different models to visually communicate the future state. This includes scope models to understand boundaries and stakeholder maps to identify those who might be impacted by this work.

Conceptual thinking skills help analysts understand the big picture and provide the context for the analytics work. Interaction skills, communication skills, analytical thinking, and problem-solving skills are useful when leading discussions to identify metrics and establish objectives.

Real-World Problem in Defining the Future State for a Predictive Classification Problem Detecting fraud is a perennial problem in multiple industries such as banking, finance, insurance, and telecom. It is a typical use case in analytics called binary classification. That is simply saying a particular transaction based on the analytics model is classified as fraud or not. For such a problem, the measure of success is governed by the business context and the identified business problem which the analysts formulate while defining the future state. There are some standard measures such as precision, recall, specificity, or accuracy that are commonly used for such types of problems described by the following formulas: Precision = TP/(TP+FP) Recall = TP/(TP+FN) Specificity=TN/(FP+TN) Accuracy=(TP+TN)/(TP+FP+TN+FN) Where: TP = True Positive. The number of transactions predicted as fraud which are actually fraudulent. FP = False Positive. The number of transactions predicted as fraud but are not fraudulent. TN= True Negative. The number of transactions predicted as not fraud and are not actually fraudulent. FN = False Negative. The number of transactions predicted as not fraud but are actually fraudulent. Consider a scenario where a business wants to detect fraudulent transactions for credit cards. There are many factors (transaction time, location, and amount) which influence a transaction to be classified as fraudulent. When this type of fraud is detected algorithmically, there is a possibility that many transactions will be misclassified. A transaction may be predicted as fraud but in reality, it may be a valid transaction (a false positive). Similarly, a transaction can also be misclassified as a false negative. Depending on what the business wants to achieve, the criteria of success for the fraud detection analytics model may change. If the business wants to detect as much fraud as possible, the analytics model is adjusted so that the maximum number of true positives are detected. But, this also increases the chances of false positives. If the business stakeholders take a conscious decision that false positives are not a concern then the analytics model may only focus on precision as the most appropriate metric to maximize. On the other hand, the business may want to define success as a measure of the actual cost to the company. The actual cost would be a trade-off between cost saved by predicting fraudulent transactions versus cost incurred for incorrectly predicting a fraud (cost of false positives and false negatives). In this case, precision will not be the right metric to pursue. The key takeaway for analysts from this discussion is depending upon the business context the success criteria of an analytics initiative changes. The analyst must be able to articulate business context to the analytics team and similarly, explain to the business stakeholders any mathematically complex measure in simple business terms.