How to Design a Dashboard for a customer

 

1. Do NOT sell flashy stuff for the sake of appearing “trendy” or “up to date” . “Up to date” gets dated pretty quickly. Useful, on the other hand, usually endures.

 

2. Use common sense more than you use the solution vendor’s marketing material.

 

3. Ask a few questions:

 

A. What are your categories of users? (Line managers, Middle Managers, Top Management, HR Business Partners, HR Senior Managers, Finance Managers..)

 

B. Do the information needs remain the same through the year, or do they change according to your time of year (financial year end closing, Appraisal year end closing, quarter end closing of sales…)

 

C. What is the top DECISION SUPPORT information that you need? no less than 1, and no more than 8. For each category of user. Gather this information using questionnaires divided by user category. Arrive at the top (by mode) answers and finalise them.

 

D. How often does this information change enough to impact ur decision making? (Eliminate all information that does not change significantly for a month or more. e.g., gender diversity is a statistic that will not change for the rest of the year, except during the campus recruitment season, when we need to actively monitor if we are hiring enough from each gender)

 

The second use of this information, is to determine the refresh rate with the database.

 

E. Do you want the dashboard to be the opening screen, or do u want to access this on need basis? (Tells you a lot about the actual usage of the dashboards being designed) . Please get this information, again, by category of user, then determine. Some categories may want it to be the first screen, others may want more than one dashboard that they access on need basis.

 

F. NOW, go to the flashy stuff – the look and feel.

 

What is the process you would like to follow, as an Analytics consultant?

 

 

 

Employee Engagement and Productivity – The role of the employee personality

 

This morning, i set self a small challenge – Does employee engagement have a positive correlation with productivity? Can employees be productive even if they are not engaged? Can they be engaged without being productive? (think public sector in India) .

 

In most research studies (see links below) Engagement is almost always found to be positively correlated with Productivity.

 

But what if, it was possible for employees to be productive without being engaged? What if they brought to work  – not their personality, but their experience and expertise?

 

Surprisingly, this was the predominant school of thought in the manufacturing era, when we expected people to leave their personalities outside the door, with their shoes, and to wear them again on the way out. Inside, they were time and motion machines (think Frederick Winslow Taylor and the One Best Way theory) .

 

In the IT era, we said, we are hiring brains and not time and motion machines. And yet, we continued to do effort estimate on “man days” and “man hours” – based on the “average time it should take a person to do this task”.

 

Coming back to the subject, does an employee have to be engaged to be productive?

 

I think that productivity is a function also, of the personal discipline and professional ethics of the employee. Of course, here we assume that the employee has the relevant experience, expertise, and authority, and all the organisational factors have been taken care of. Those are hygiene factors in any discussion on incremental productivity.

 

Without any employee engagement measures, using the pure “Work-for-pay” model, the output is:

 

Work = Pay

OR

Work  < Pay

OR

Work > Pay

 

What are the factors affecting this equation and the direction it tilts in?

The employee’s personal engagement level, his/her personal traits, since all employees are treated the same, but some are on the left of the equation and some on the right.

 

So, at least some part of the engagement quotient comes from the employee – from their own personalities.

 

When doing any engagement initiatives, the organisation has to target them, not towards general theories of psychology and Organisational Behavior, but towards the kind of employees they have hired in the first place. The extra benefit obtained from each engagement dollar is also a function of the fit of initiative to the personalities of employees.

 

In a small organisation, it is self evident. And in large organisations, this principle should be exercised using a simple breakdown process – let each sub organisation decide what works for them. Monitor results, correct course where required, but do not assume that the entire organisation has exactly the same kind of people.

 

Now, lets assume positive correlation between engagment and productivity, such that, for every dollar spent on engagment, the producivity does go up, only the scale is unpredictable.

 

i.e.,

Pay + Engagement = Work + x

where x is the additional productivity created by engagement initiatives.

 

Question to ponder: Can x be a negative value? Can engagement intiatives backfire and make employees even less productive than they would otherwise be? What do you think leads to negative values of x?

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Further reading

 

Here is the set of online resources that mentions other studies done on the subject:

http://www.workforce.com/article/20130501/DEAR_WORKFORCE/130509997/how-do-we-know-our-engagement-efforts-are-paying-off

 

http://www.insyncsurveys.com.au/resources/articles/employee-engagement/2012/10/impact-of-employee-engagement-on-productivity/

 

http://www.sciencedaily.com/releases/2011/07/110720142459.htm

 

http://www.sciencedaily.com/releases/2009/05/090513121050.htm

Predicting Employee Attrition Using Big Data

 

2 weeks ago, the HR Head asked me a question – I want to know which employee is going to put in his papers – and when.

 

It was one of those rare moments when i was completely, totally stumped.

 

Here is a partial answer- How to use Big(and small) data together to predict employee turnover.

 

Factors that impact turnover

Lets start at the very beginning. What makes an employee quit?

 

When I conducted Exit interviews at our small 700 people IT organisation, i wouldnt ask them, “why are you leaving?” I would ask, “Why did you start looking?” I wanted to understand where the distance began, and why. The resignation is not what we are investigating. That action is the result of a disengagement that began weeks, months, even years before the actual resignation.

 

We are investing that disengagement. And the probability of its resulting in a separation. Two different things.

 

How do we measure something as intangible as disengagement?

 

I believe we may have some ideas here.

 

Pointers to Disengagement

 

• Employee Satisfaction Scores

This one is apparently a no brainer. Yet I am suprised to see that most ERP packages dont have a place to store the employee’s engagement score and compare that year on year. Then check for its correlation with their performance ratings and other behavioral actions. There are pointers there.

 

• Social Media Activity

This is where big data comes in. Have an internal IM program and an internal social media platform like yammer or internal discussion boards? Let your Big Data analysts do quick calculations on how often and with how many colleagues the IM was used. And how often the social media platform and discussion boards were used. Engaged employees will use more “connection points” to connect with the organisation and its people.

Notable Exception: Introverts. Introverts are people too. And they wont use Social Media. The end of this post says “Its the pattern, not the static data.” Read that section to know more.

 

• Meet the Parents

This is one of my favorite metaphors. When they bring the family, they are engaged. No exceptions. This also can be automated. Attendance at the family events is automated and can be fed into the giant supercomputer for automatic analysis. If you know when they stopped bringing the family, you know when they started thinking out.

 

• Access Card Patterns

Another big data beauty which needs individualised reading to make sense. How often was the access card used to go in and out? Whats the pattern? Has it changed lately?

 

Which access cards are used together? Are the breaks in groups, with one or two friends, or alone?

 

• Use of development resources on the learning portal

What kind of courses are being accessed? What was it earlier? Is it consistent with the expectations of the current role? What is the usage pattern?

 

• Correlate with Performance Ratings and the moneys

The higest risk categories are employees who have recently witnessed a fall in the rating, or whose difference from their maximum potential earning is very high. Let me explain. Suppose Mr. Alpha is paid INR 100 at the highest paying company. You are at the 60th percentile as an organisation, so you pay INR 60 for the same profile. But suppose the actual salary of Mr. Alpha is not INR 60, but INR 55, because of your internal compa ratio adjustments. Which means that Mr. Alpha is at a 45% discount from his maximum earning potential. That kind of gap is not sustainable.

 

And lastly, remember, its the pattern, not the static data. Big data will, over a period of time, establish patterns of behavior for each employee. When this pattern of behavior changes in a perceptible way, and for a consistent period, you know you should care enough to investigate more.

 

Does disengagement always result in attrition? Is it worth bothering with if it doesnt lead to attrition? What are some of the other pointers that can be used to arrive at behavioral disengagement? Anything we have missed out in the article above?

The Monetisation of Social Capital

 

As we write, Twitter is likely to be valued at 11 billion. Facebook’s current market cap, according to this article, is at 100 billion.

If you were an old grandmother somewhere in (any part of the world), who was a nosy Parker and knew something about everyone’s affairs, obviously you could guide a traveling salesman to the house that is most likely to buy the stuff he peddles. But the question is, how much would that traveling salesman be willing to pay you for that information?

 

And the answer is – 100 billion? 11 billion? 340 billion?

 

What is the point? Bear with me while we explain this.

 

The economic value of social capital is a very new phenomenon for the human civilisation. Social capital has always existed, and has helped society immensely – as traditional medicine, gossip (and what would we be without gossip?) , product recommendations from friends (remember Amway and Avon?) .. et al.

 

For the first time, we see a price – a very substantial, tangible price, being put on the notional value of this information.

 

Is this information, inherently, worth the price we put on it? How much more do companies sell on targeted advertising vs generic advertising?

 

I do not have an answer. Given the size of global commerce, the numbers could be too low, or too high, depending on that crucial factor – IS the grandmother’s knowledge of which house to go to, leading to a sale?

 

And i really think we should pause and ponder.

Social capital also has a social cost.

The second factor in the monetisation of social capital

 

The second factor, quite simply, was that we had the computing power and the algorigthms needed to get the juice out of those digital social conversations.

This mass of data could not possible have been analysed in this way 20 years ago. Even if someone had, at that point, created a facebook, it is unlikely that we would have seen the value of this social capital in 1993.

The critical thing is, that with the advent of big data, came also the ability to handle it productively. (or, at the very least, as productively as we are handling it now).

Together, the digitised social interactions and the algorithms, create a notional value which has a definite financial worth attached to it.

Concluding question: Who owns these social interactions? The generators of content, or the aggregators who provide a free space, and then aggregate that content for their profits?

Using the Talent Pool without having to hire them

 

Suppose there was a way by which you could assess the technical competence of a candidate even without putting them in an interview?

 

Suppose further, that by this method, you could also get the person to contribute to projects in your organisation without getting paid – on their own time?

 

Suppose that this tool also allowed you to actively stay in touch with your alumni, with the possibility of rewarding them for continuing to be associated with your brand?

 

You are most likely to say that is impossible. And for the most part, it is. Which is what makes this tool so awesome.

 

The Magic..

The tool is a discussion board, open to internal and external folks, enrolment purely voluntary, and all participation rewarded with points.

The points can be converted to monetary and non monetary rewards. Monetary rewards are gift cards from selected vendors. Non monetary rewards are invitations to employee only events (for non employees), membership to industry bodies sponsored by the organisation, and so on.  

 

When you face a business or technical challenge, put it up. Let people respond. All participation rewarded, and all productive answers rewarded extra.

 

Let it be everyone’s playground – from the junior to the senior most person, let everyone talk about strategy to maintenance, from diversity to facility management.

 

The Numbers

Crowdsourcing of ideas is not new. But here is what this model has in addition to ideas:

1. Rewarding Engagement – internal and external, in tangible ways.

2. Pre selecting talent on the basis of their actual contribution and not just on the basis of their interview performance.

3. An opportunity to notice skills/ideas of employees that they are not able to demonstrate in course of their normal work.

 

On the cost side of the equation is the cost of building and running such a platform. Or buying one. On the benefit side are intangible benefits that quite simply are not available elsewhere.

 

The maths makes the most sense for a mid – large sized company in niche skill areas like audit, acturial, IT, Energy, Infra, Manufacturing, community building, city planning, e governance.  

 

The maths does not make sense for generic skill organisations or organisations that depend largely on cottage industry inputs for sustenance. It also does not make sense for talent communities with low penetration of computers.