What is Analysis Paralysis?

Ivy Aug 06, 2014 No Comments

As ‘policy paralysis’ becomes the latest euphemism used by our ingenuous Indian politicians, it reminds me of the synonymous jargon used within many analytics mature organizations – ‘analysis paralysis’. This is simply a polite way of referring to the state of over analysing, so much so that the process of decision-making freezes (or paralyses) and the business operations are affected adversely due to inaction.

Investopedia defines analysis paralysis as

“when an individual becomes so lost in the process of examining and evaluating various points of data that he or she is unable to make a decision with it.”

At a time when analytics is making waves – applied to management decisions ranging from inventory to sales, employee compensations, production process, pricing and more – there is often the risk of reaching a stage when information overload may paralyse the decision-making process.

How do you know when your organisation or client is afflicted by analysis paralysis?

# When the available options seem to overwhelm the management or decision-makers.

# When a long time is spend on thinking over every single decision, because of fear of making a wrong choice.

# When the need is felt to analyse every single option before making a decision or arriving at a conclusion.

# When over-analysis becomes a habit, often stopping quick decisions, sometimes even missing good opportunities.

# When indecisiveness, drains the management of time and energy.

# When even simple decisions are over-complicated because of  the compulsive need to analyse and research.

How to deflect analysis paralysis?    

• Differentiating between the issues under consideration – how important it is, what is the impact in the long term, does it need to be addressed immediately
• Identifying the business goals – in an immediate, mid and long term perspective
• Allocate budget for the issue under consideration, in tune with its importance to the business operations
• Learning to eliminate bad options at the first instance
• Fixing a time-frame for problem resolution or decision-making, and sticking to it
• Picking out the best option available within the time-frame and budget, and refraining from second-guessing
• Going by gut instincts when time and money is limited, or the matter calls for instant action
• Building a good team on whom you can rely for timely feedback and effective actions
• Planning your analytics roadmap keeping a real connect between various departments, business goals, data applied and span of problems addressed

While this applies to businesses using in-house professionals and customised software for its analytics; it is equally applicable to firms that outsource their data and analytics requirements to consulting firms and management professionals.

The professional in the role of business advisory / analytics support has an important obligation – that of maintaining the right balance when steering the client’s data and analytics strategy.

Thumb rules for asking the right questions to support the shortest story-board design and optimal analytics usage

• What is the exact nature of the client business – Analytics deployment differs for industries as well as application scenarios. So analytics solutions for a retail store would differ from that for a hospital / healthcare business. Again, analytics for addressing the problem of fraudulent transactions in a bank would be distinctly different from that used for devising a pre-Diwali marketing plan of a consumer appliance manufacturer.
• What problem the client needs to address – Does a retail store need to plan shelf space optimization for translating footfalls into sales? Is the healthcare client concerned about fraud occurring in its stores supply department?
• What analytical process would best deliver the client needs under consideration – Every situation or problem has more than one way in which analytics can be deployed to facilitate decision-making. So if the client is concerned about poor sales performance of his recent launch, then would social media and sentiment analysis help understand the problem? Or would marketing analytics work better to gain a 360⁰ view of customers and suitably align business or product strategies?
• What is the budgetary allocation of the client – While clients with larger budgets can facilitate adoption of various real-time data and analytics across its business operations, there is always a risk associated with analysis paralysis. A balanced  analytics programme or BI roadmap regardless of budget, is critical to business success.
• Does the available data suffice the needs of the business? Is it judiciously integrated within the business process of the client? Does the data support the real-time or predictive needs of the client?
• What is the framework within which the client needs to address the problem – The time framework largely determines the analytics solution deployed to fast-track the problem.
• How mature is the business process – With business maturity, data becomes voluminous. Analytics is also used at various levels of business operations and departments, very often built-in within the enterprise system. Such situations call for skilled application of analytics for an effective business ROI.

Bottomline – Ultimately, the ability to respond quickly to situations, is what drives a business forward.