data managementData Based Mindsets – Maturity Levels

Data Based Mindsets – Maturity Levels

Data Based Mindsets - Maturity Levels

As of 2021, organizations have heavily invested in data infrastructure and supporting resources. But it’s grim to hear that nearly 85% of all data and analytics based transformation projects fail to meet their objectives.

There can be a long list of reasons for this state of affairs, but an undeniable contributor and a systematic issue is the organization’s culture – A lack of a Culture of Data Based Mindset.

Many times when we don’t know what is the problem with the organization, we tend to brand it as a cultural issue. So to say, the problem is attributed to Data Based Culture or lack of it, is a biased statement without going deeper into the issue.

The willingness of employees to accept & adapt to the new way of working. ie., taking decisions or actions based on data, is in fact the real barrier and reason for low adoption of data based transformation projects.
This is one of the new pillars of professional excellence – being fluent with data and making it an integral part of day to day actions.

Data Fluency, Data Literacy, Data based Mindset – whatever name we want to give, it boils down to how comfortable we are with data.

Like any other new technology – mobile, web applications, smart devices, social media, etc., we have a continuum of users of different maturity and proficiency in Data Based Mindset or Data Literacy too

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If you wish to honestly self-assess or evaluate your team on where they stand when it comes to using data for decision making, a Data Based Mindset Maturity Model that covers data literacy maturity levels such as the one below will be very helpful. We’ll now look into the levels of Data Based Mindsets:

Data Based Mindsets - Maturity Levels

Resist – confused beginners

At this level, at heart, an individual is a disbeliever in data based decision making. Such individuals would hardly look into data but when they do so, they try to run a variety of logical counter-arguments to discredit data and its insights. One can say that they have a strong belief system that is anti-data or facts, which is based on biases, preconceived ideas of the situation, hear-say facts, anecdotal evidence, etc. Folks at this level offer maximum resistance to change. At the deepest level, it can be on account of fear of some kind. When an organization has many key stakeholders and king-pins in this bucket, the organization would more or less be a data-laggard. Ultimately they will have to take off from this level due to peer pressure and so ‘confused beginners’ is a fitting description for this group. 

Access – social drinkers

At this second level, individuals are limited users of data, only based on necessity. I’m tempted to compare them with ‘social drinkers’. Situations and environment forces them to consider data and they do so with reluctance. Naturally, they are users with limited proficiency in data handling and are uncomfortable when placed in data rich discussions.

User – consumers and cheerleaders

Bulk of employees in organizations that are doing well today, fall into this group. They believe in data based decisions. So there is no mental block with this group. But they have skill limitations. I’m tempted to compare them with the audience and cheerleaders in a football match. They love the game, they enjoy and relish talking about it in great detail but they’ll always be in the stands and never put their foot on the playground. Either they don’t have the skill to play or they are too old or not physically fit for the sport. But being here is not that bad from an organization’s perspective. They are willing to consume data and inferences given to them and believe in it.

Understand – power users

They are individuals who have good expertise and are strong believers in data. They lead with data, whether it is about analysis, framing scenarios, statistical and probabilistic analysis, considering divergent views from data, they are very proficient. You can take them as ‘power users’. At extreme, they may tend to be ‘annoying experts’ too :-). 

Create – prosumer

Standing at the highest maturity, this group may not be really senior in organizational hierarchy, but they definitely are good at synthesizing deep insights from data, applying unconventional & innovative methods to generate unique value propositions for business and customers that can disrupt the current state. They look through the data beyond the obvious. You can call them the ‘prosumer’. They are both the producers of insights and its consumers. As a result, they are the trend setters. Obviously there are going to be only a few fitting into this group. 

This understanding of the Data Based Mindsets & Maturity Levels can be applied to evaluate its relevance to your industry, specific roles & departments and their ideal maturity states, skills needed and critical mass needed at each level to build a Data Based Mindset Culture, etc. 

If you wish to learn more about evaluating your organization on Data Based Mindsets & Maturity Levels or create a roadmap, feel free to contact us. 

If you are looking for Data Analytics Workshop in Chennai, Bangalore, Mumbai, Delhi and across India, contact us.

Sales Analytics Framework

The Analytics Framework for Sales aims to support a long term strategy of applying analytics in sales and to integrate seamlessly with remaining part of the organization’s analytics strategy.

There are 3 important pillars for effective deployment of Sales Analytics Framework. A framework like this, when implemented consistently, will shift Sales Analytics from a tactical business enabler to strategic business enabler.

Sales Analytics Framework

There are two important prerequisites that act as a foundation for the 3 pillars and at least one over-arching philosophy to derive best out of the Sales Analytics Framework. Let us look at what are they:

Business Benefit of deploying Sales Analytics Framework:

Business Growth

The ultimate business objective of using Sales Analytics to help meet organizational long term and short term goals such as Market Share, Revenue Growth, Profitability, Customer Retention, Cross-sell, Upsell, Referral business, etc

Overarching Philosophy:

Change Management & Data Based Culture

Ultimately leaders play a big role in deciding how the outcomes of analytics are put in practice during the decision making process. Businesses have their priorities that are time sensitive and hence sales directors play a big role in deciding how these would be actually applied. Driving a culture of data or fact based decision making within the organization should be one priority when it comes to sales analytics. The benefits of sales analytics grow multi-fold when cultural change occurs.

The Pillars:

Data Management

The first and foremost success factor in Sales Analytics is the ability to acquire, clean, organize, integrate, describe, share, govern and store data to be used for analytics. Data is usually acquired from various sources. Most organizations hit a road-block right at this stage of the journey. The next step is to clean and integrate data from various sources so as to enrich its value before performing required analytics.

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Sales Analytic Processes

The core sales analytic processes include the ability to enrich the data through advanced analytics & data science and to provide, visualization, exploratory data analytics (EDA), business insights to drive sales, develop predictive models such as propensity, lead scoring, up-sell, cross-sell prediction, etc. The ongoing optimization and deployment of models in the production environment and their regular refinement are all integral to Sales Analytic Processes. The element of focus for us in Sales Analytics Processes is to answer some of the design questions keeping in mind the above end states, such as Measure of Success of Analytics Program, what analytics are we going to perform, what data and models are we going to use and how will we share the results. Learn more about How can Sales Managers leverage Analytics?

Decision Engines

How do analytics capabilities deliver business value. Traditional outcomes of analytics decision engines were linked to information delivery and visualization enabled through Online Analytical Processing (OLAP)and data mining. On the other spectrum, insight discovery and integration to decision making processes are capabilities that add business value.

The Prerequisites

Sales Analytics Infrastructure

To start with organizations can work with minimal infrastructure for analytics, but to scale analytics capability, definitely investment in infrastructure is a must. Analytics Infrastructure, not restricted to Sales Analytics, includes databases and data warehouses, statistical and data mining systems, scoring engines, grids and cloud storage ,etc., The question that new entrants frequently ask is, is it not possible to start Sales Analytics without these. The answer is that it is possible. With SaaS, it is possible to put together low cost infrastructure to get started with sales analytics. In addition to storage and handling, software tools required for sales analytics are also part of the assets. While there are standard and custom tools, the best to start would be Tableau, R, Python, SAS, RapidMiner, Orange, etc. Learn more about Guide to Selecting Sales Analytics Software Tools

Sales Analytics Talent & Capability

Quite often this is the most ignored area in analytics and is also a pitfall. Organizations get neck deep in creating analytics infrastructure, acquiring talent to manage these, and analytics talent with IT capability to deploy these tools, but they fail to understand that business users are really the ones who need upskilling. If they fail to recognize and resist the usage of analytics in their daily work management, Sales Analytics will only remain a pilot project to showcase. Lead More about What specific analytical skills are needed for Sales Managers in the era of Analytics?

If you are looking for Sales Analytics Training and consulting in Chennai, Bangalore, Mumbai, Delhi and across India, contact us.

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