Simplifying Analytics Adoption for Decision Making

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The above statement very aptly emphasises the importance of data in decision making.

Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.

Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?

Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data.

I believe in God! For everything else, come with Data

The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.

Consider a situation where the organization A makes use of sales data at the “wholesale” level for making production planning and inventory optimisation. Organisation B makes use of sales data at the “Retail level” for the same tasks. Organisation B, which has better information about frequency of product consumption at the most granular level (retail level) is likely to forecast better than organisation A.

Yes, the organisation's objective is to sell to the “wholesalers” only but the wholesaler will in turn place a new order only when his stock gets cleared and this applies to all downstream levels until the product gets sold to the end user of the product.

Emphasis on the need to make use of information to help organisation's growth

In a global survey of 1,375 subscribers conducted by Harvard Business Review Analytics Services in January 2010, 85% of respondents said that information is a key strategic asset, yet only 36% said their organizations are currently well positioned to use information to help grow their business.

The disparity at the upper end of the scale was even more dramatic; while almost half—45%—strongly agreed that information is a key strategic asset, only 7% believed they are very well positioned to exploit it.

Source: HBR Analytics services: Unlocking the value of the information economy

Considerations for data driven decision making

Unless data gets used in its most granular form (close to customer) at all managerial levels, it is very difficult to fully benefit from the fruits of analytics adoption. When granularity of information is compromised, our decisions can't be accurate. Adopting analytics only at select managerial levels can be a misleading exercise.

Timing crucial business decisions on changing customer needs is of paramount importance. Granular level customer data helps point out changing customer needs and behaviours.

Why is it disastrous to use analytics only at select managerial levels?

In the decision value chain, information flows from the bottom level and cascades upwards. Top level managers make use of this information to take decisions and form strategies. In most organisations, top level managers have access to BI tools to slice and dice data, but the middle level managers are not equipped with tools needed to take data backed decisions. They can neither pull granular data dynamically, nor validate the claims made by the lower level managers. The claims made by the lower management, when cascaded upwards might paint a wrong picture about crucial metrics. Hence the strategies devised by the top management could go for a toss. Unfortunately, even today, this problem remains unaddressed in most organizations!

Emphasis on enterprise wide analytics adoption

“More than 70 percent of the organizations that had deployed analytics throughout their organizations reported improved financial performance, increased productivity, reduced risks, and faster decision making.”

The Evolution of Decision Making: Harvard Business Review Analytics Services

Is it really difficult to adopt analytics at an enterprise level?

Rapid advancement in computer hardware capabilities has certainly ensured that it is now possible to scale up the usage of Analytics at an enterprise level, but it does not facilitate analytics adoption at the enterprise level, because it is not that easy!

Let us talk about the pain points involved in implementing analytics/ usage of analytics tools at an Organisation level:

Need for special coding skills for performing Advanced Analytics.

Inherent complexities of the Analytics tools Strong understanding of statistics Huge training and prohibitive licensing costs. Significant change to existing organizational

processes Huge time for implementation Resistance from the not-so –techy managers

towards analytics implementation

Obviously you can't expect even modern day managers to start using Analytics tools for dec i s ion mak ing ! No wonder, in most organisations such costly analytics tools go un-used. Given the prohibitive licensing cost, “Analytics adoption” becomes a distant dream for smaller organisations as well.

What should the solution look like for enterprise wide analytics adoption?

A solution suitable for enterprise wide adoption must have the following characteristics No significant changes to the existing

organizational processes No need for special coding skills A basic understanding of statistics needed to

comprehend results An easy to use consumer app like tool to

perform statistical analysis Very short learning curve Zero or Minimal dependency on external

agencies Lower cost of adoption and implementation

Few decades back, such a solution was a distant reality. Today it is well within reach. It is now possible to pack the power of complex analytics behind a simple consumer friendly user interface. This can also significantly reduce the learning curve, training costs and time period for implementation. “Cloud computing” now gives access to high end hardware at minimal costs. This advantage can encourage organizations to think about enterprise wide analytics adoption. Add to this, a “consumer app” like tool that lets managers dynamically validate granular data and perform advanced analytics using simple drag drop operations.

Why invest in cloud based solutions ?

In a new global survey of nearly 1,500 business and technology leaders conducted by Harvard Business Review Analytic Services, the majority — 85% — said their organizations will be using cloud tools moderately to extensively over the next three years. They cited the cloud's ability to increase business speed and agility, lower costs, and enable new means of growth, innovation, and collaboration as the drivers for this fairly aggressive rate of adoption.

HBR Analytics services -How the Cloud Looks from the Top: Achieving Competitive Advantage In the Age of Cloud Computing

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Case

“Surveys” are a commonly used by organizations today to collect data that is closest to the customer. There are many tools in the market today that can execute surveys and perform first level analysis. Such tools however don't deliver advanced analytics and significant statistical interpretations. On the other hand, the Analytics tools that have advanced analytics capabilities, are not easy enough to be used by managers and moreover, demand significant long term investments for licensing and training.

Solution

Imagine a manager being provided with a tool that performs all the complex survey data analytics in the background and delivers insights such as - “The respondents have given a low recommendation score for our company's products and services due to the reasons “Difficult to access”, “High turnaround time”, “High cost of servicing” etc. and the following are the respondents' opinion “Poor IVR”, “CSR's lack technical knowledge”, “Poor After sales service” etc.”

In technical terms, we call the reasoning part of it as “Key driver Analysis” and the opinion part as “Verbatim analysis” or “Unstructured data” analysis. Today, in the survey analytics space, performing advanced analytics with “drag & drop tools” and getting actionable insights (such as the one mentioned above!) within hours, is a reality. It empowers middle level managers in their decision making and helps them validate the data. It also serves as an assistant for data scientists. The bottom line is “timely decision making” and “affordability”!

A live example of an “easy-to-adopt” solution.

Surveyi2i is the perfect example of a manager friendly tool which enables dynamic insight generation from survey data. Surveyi2i is a cloud-based one-stop shop for all analysis and reporting needs from survey data. Surveyi2i enhances productivity for analysts and researchers. Business managers can derive insights quickly without help from data scientists. Surveyi2i enables financial institutions to very easily implement data-driven customer and employee engagement strategies by understanding customer needs and experiences better at negligible costs.”

To know more about Surveyi2i visit http://www.bridgei2i.com/surveyi2i.html

About BRIDGEi2i

BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. BRIDGEi2i has featured among the top 10 analytics and big data start-ups in several coveted publications.