Data interpretation
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published July 5, 2018
I would like to share with you the theme of data interpretation. For many years I had to work actively with data. I met different cases while writing reports for users and top management, analyzing requests for reports and the customer's requirements.
I often see analytics making clear conclusions, but they are quite controversial. When composing a request for analysis, they try to consider the data only from one perspective. That's why I would suggest you look at your query from some other angle and find different conclusions.
I would like to share with you the theme of data interpretation. For many years I had to work actively with data. I met different cases while writing reports for users and top management, analyzing requests for reports and the customer's requirements.
I often see analytics making clear conclusions, but they are quite controversial. When composing a request for analysis, they try to consider the data only from one perspective. That's why I would suggest you look at your query from some other angle and find different conclusions.
How does your management see your data?
Some digits and rectangles of different sizes. Without any comments and explanation, it is an utterly unnecessary picture. The main conclusion which an analyst likes to make: if something grows, it means it's okay; if it falls, then it's terrible. Analysts often choose for the bosses only that increasing data trends without any explanation. And what has grown? Why? What does it mean? If an analyst does not have answers to these questions, then this is a mistake.
There are many examples where the trend is growing, and this is bad. For instance, if we consider the situation that the number of appeals or complaints is increasing, then we are working poorly, and something is going wrong. If the period of requirements gathering and clarification from a customer is growing, in this case, development stands still.
Some digits and rectangles of different sizes. Without any comments and explanation, it is an utterly unnecessary picture. The main conclusion which an analyst likes to make: if something grows, it means it's okay; if it falls, then it's terrible. Analysts often choose for the bosses only that increasing data trends without any explanation. And what has grown? Why? What does it mean? If an analyst does not have answers to these questions, then this is a mistake.
There are many examples where the trend is growing, and this is bad. For instance, if we consider the situation that the number of appeals or complaints is increasing, then we are working poorly, and something is going wrong. If the period of requirements gathering and clarification from a customer is growing, in this case, development stands still.
Making decisions
It is necessary not only to receive data but also to make decisions based on the information received. Otherwise, it is an analysis for analytics, i.e., a waste of time. When I processed requests for various reports in a telecommunications company, I usually asked for what purposes you need this data. First, it helped to weed out unnecessary queries — i.e., if a person does this just because someone told him, and he does not understand
It is necessary not only to receive data but also to make decisions based on the information received. Otherwise, it is an analysis for analytics, i.e., a waste of time. When I processed requests for various reports in a telecommunications company, I usually asked for what purposes you need this data. First, it helped to weed out unnecessary queries — i.e., if a person does this just because someone told him, and he does not understand
or is not interested in the final result, I would not want to spend my time on it.
Secondly, a person can miss an indicator, watching the situation on his part, which will not give him a full picture of what is happening. For example, suppose an analyst has the primary task to check the marketing campaign's efficiency. He will ask only the data relating to the promotion period and the subscribers who fall under it directly. But he may not think that at the same time a discount was launched, and his marketing campaign does not bring enough revenue because some subscribers are served at a reduced price. Finally, the result will be misapprehended; it will be impossible to draw conclusions and take corrective actions.
Data forecasting
An excellent skill is data prediction. If you already have some quarterly / monthly or weekly data, then, based on the average, you can predict how the system, subscribers, or revenue will behave.
A good example is the evaluation of labor costs for the customer, especially in the commercial version. For instance, we have a project with one developer; his price is $25 per hour, the duration of the work is 6 hours or 150 dollars a day. Every day we ask the customers three new questions and get only one answer. Each answer is one hour of the developer's work. By the fifth day, we have eight unanswered questions, or 200 dollars, wasted for "the work is in place."
In total, we can say that the developer works 4 hours a day and sits idle for 2 hours. If this continues, then for a period of 30 days we will have a situation that accumulates 48 unanswered questions (30 * 8/5). The customer will pay a total of $ 4,500 (30 days 6 hours of work at $ 25 per hour), of which $ 3,300 for useful work and $ 1200 for idle. This example, of course, is very crude, but it perfectly tells the customer how much his silence costs. There are, of course, some clients who are satisfied with it.
Tracking Results
If you've received the data and drawn conclusions, it is necessary to take some further action. And after that, you must always track the results of your manipulations. For example, you have an inefficient site; it does not bring you the right amount of orders. You decided to spend a budget of $ 1000 on setting up your site's SEO. After a while, the management asks what the result is
- It seems the site works better. We now like the site more.
And how much is it in percentage, in numbers? Due to what? What did you do? What actions have been taken? Without specific figures and analysis, this does not provide answers to all the questions posed.
There also might be a problem with the executor. You convinced superiors to give you a budget for optimizing SEO but did not look at the results or looked but did not make any conclusions. And most importantly, they did not bother to tell the result of the analysis of the effectiveness for their superiors. The next time you are unlikely to have approval on this application. The big boss does not understand what SEO is, but there is no efficiency from it. Why should we spend any money on it?
Who needs information, and what are the goals of this?
The first question that you should ask yourself when preparing the analysis is who needs the analytical data that I will provide? Often, top management does not require data on one subscriber's revenue or data on one employee's inefficiency unless the task was about it. The management wants to see trends, comparisons for the period, conclusions, and specifics.
If you need to familiarize the new employee with the work process, you should show him all the details. For example, you may reveal the data sample from data sources, then turn it into accumulative indicators, compile a report, and describe each indicator's components.
Perhaps you provide data for an employee who works with complaints—data are showing that there is incorrect traffic charging for a group of subscribers. You have to understand the details of charging on one subscriber's example to go through the whole process and see all the services, traffic, charges, etc.
The second question that you need to ask yourself, what conclusions does an analyst have to make based on your data?
As a rule, if I come to the management with some complaint and do not provide any solutions, this looks like a general discontent. If you show figures, the administration should understand why they need it, what follows from this, and hear your suggestions on this matter.
Options:
1) Harvest of garlic has grown on the outskirts of city A. So what?
2) Harvest of garlic has grown on the outskirts of city A. They are the leading suppliers of products to the city B. Last year there was a shortage of garlic, so we decided to earn on it and exported 300 tons of garlic there. Because city A has a high yield this year and starts dumping prices drastically, our income from the exported product may fall sharply. Therefore, we propose to distribute a part of the exported product to city C, in which there are no products from city A, this will help us … and so on.
Problems of interaction
This is a common problem for large companies. For example, I analyze the data to decide on the launch of a new marketing campaign. I run it and switch to the next run analysis. At the same time, I spend corporate money on advertising and employees' time to create and process it. The review of the campaign's efficiency is handled by another department, which does not influence me. Time after time, they see the analysis results displaying ineffective campaigns or promotions but do not sound alarm, do not give recommendations, and do not ask management to take decisions. Chariot turns, work is done, efficiency is zero.
Strange data
If you received data that shows you a big splash, and most importantly, you exactly got the result you were expecting, do not rush to run with this news to the leadership. I would suggest that you look at this data for a period, compare and thoroughly dig into the causes.
For example, you launched a new tariff plan and predicted that its efficiency would be the revenue of $ 500 per week. We decided to wait two-three weeks and, after that, conclude. You request income in 3 weeks and see that the first week's revenue is $ 50, the second is $ 60, and the third is $ 800. I knew it! — you think at this moment. If I were in your place, I would begin to doubt the correctness of this data. Perhaps there was a failure in the billing system? Maybe the one who collected the information for you today did not get enough sleep and put an extra zero by mistake. And if all the indicators are correct, then it is necessary to find the reason for such growth. There is a high probability that there are some factors that caused this splash. You can safely go to the managers and say — the revenue from this tariff is $ 300 per week. Still, this figure cannot be considered fair because there is a football championship in the country, and many tourists arrive in a couple of days and use SIM cards with this tariff plan. And if you exclude this week from the analysis, then the average revenue will be $ 55 per week.
Ambiguous indicators
There are cases when it is necessary to make an assessment, but there is no exact method from calculation and analysis. For example, we do not have single-valued KPIs for calculating employee performance in a sprint. One developer can close ten tickets in 40 hours, another in 45 hours will complete only three. But the problem consists not only of developers' work speed. Here it is necessary to take into account the complexity and volume of tickets. In such situations, you need to calculate the entire team's average performance and use it to conclude each employee's efficiency. Or, take the average figure of a particular employee and see if this is his standard time of work. You can not conclude with only time or quantity; you need to be more flexible and see all the factors.
For example, we're analyzing sprint planning results. What may be easier than data grouping from each developer out of the columns planned and spent? We compare data, the plan and fact practically coincide in each sprint, but suddenly, we see a big difference in one of the sprints. Does it mean that we planned the sprint poorly?
Secondly, a person can miss an indicator, watching the situation on his part, which will not give him a full picture of what is happening. For example, suppose an analyst has the primary task to check the marketing campaign's efficiency. He will ask only the data relating to the promotion period and the subscribers who fall under it directly. But he may not think that at the same time a discount was launched, and his marketing campaign does not bring enough revenue because some subscribers are served at a reduced price. Finally, the result will be misapprehended; it will be impossible to draw conclusions and take corrective actions.
Data forecasting
An excellent skill is data prediction. If you already have some quarterly / monthly or weekly data, then, based on the average, you can predict how the system, subscribers, or revenue will behave.
A good example is the evaluation of labor costs for the customer, especially in the commercial version. For instance, we have a project with one developer; his price is $25 per hour, the duration of the work is 6 hours or 150 dollars a day. Every day we ask the customers three new questions and get only one answer. Each answer is one hour of the developer's work. By the fifth day, we have eight unanswered questions, or 200 dollars, wasted for "the work is in place."
In total, we can say that the developer works 4 hours a day and sits idle for 2 hours. If this continues, then for a period of 30 days we will have a situation that accumulates 48 unanswered questions (30 * 8/5). The customer will pay a total of $ 4,500 (30 days 6 hours of work at $ 25 per hour), of which $ 3,300 for useful work and $ 1200 for idle. This example, of course, is very crude, but it perfectly tells the customer how much his silence costs. There are, of course, some clients who are satisfied with it.
Tracking Results
If you've received the data and drawn conclusions, it is necessary to take some further action. And after that, you must always track the results of your manipulations. For example, you have an inefficient site; it does not bring you the right amount of orders. You decided to spend a budget of $ 1000 on setting up your site's SEO. After a while, the management asks what the result is
- It seems the site works better. We now like the site more.
And how much is it in percentage, in numbers? Due to what? What did you do? What actions have been taken? Without specific figures and analysis, this does not provide answers to all the questions posed.
There also might be a problem with the executor. You convinced superiors to give you a budget for optimizing SEO but did not look at the results or looked but did not make any conclusions. And most importantly, they did not bother to tell the result of the analysis of the effectiveness for their superiors. The next time you are unlikely to have approval on this application. The big boss does not understand what SEO is, but there is no efficiency from it. Why should we spend any money on it?
Who needs information, and what are the goals of this?
The first question that you should ask yourself when preparing the analysis is who needs the analytical data that I will provide? Often, top management does not require data on one subscriber's revenue or data on one employee's inefficiency unless the task was about it. The management wants to see trends, comparisons for the period, conclusions, and specifics.
If you need to familiarize the new employee with the work process, you should show him all the details. For example, you may reveal the data sample from data sources, then turn it into accumulative indicators, compile a report, and describe each indicator's components.
Perhaps you provide data for an employee who works with complaints—data are showing that there is incorrect traffic charging for a group of subscribers. You have to understand the details of charging on one subscriber's example to go through the whole process and see all the services, traffic, charges, etc.
The second question that you need to ask yourself, what conclusions does an analyst have to make based on your data?
As a rule, if I come to the management with some complaint and do not provide any solutions, this looks like a general discontent. If you show figures, the administration should understand why they need it, what follows from this, and hear your suggestions on this matter.
Options:
1) Harvest of garlic has grown on the outskirts of city A. So what?
2) Harvest of garlic has grown on the outskirts of city A. They are the leading suppliers of products to the city B. Last year there was a shortage of garlic, so we decided to earn on it and exported 300 tons of garlic there. Because city A has a high yield this year and starts dumping prices drastically, our income from the exported product may fall sharply. Therefore, we propose to distribute a part of the exported product to city C, in which there are no products from city A, this will help us … and so on.
Problems of interaction
This is a common problem for large companies. For example, I analyze the data to decide on the launch of a new marketing campaign. I run it and switch to the next run analysis. At the same time, I spend corporate money on advertising and employees' time to create and process it. The review of the campaign's efficiency is handled by another department, which does not influence me. Time after time, they see the analysis results displaying ineffective campaigns or promotions but do not sound alarm, do not give recommendations, and do not ask management to take decisions. Chariot turns, work is done, efficiency is zero.
Strange data
If you received data that shows you a big splash, and most importantly, you exactly got the result you were expecting, do not rush to run with this news to the leadership. I would suggest that you look at this data for a period, compare and thoroughly dig into the causes.
For example, you launched a new tariff plan and predicted that its efficiency would be the revenue of $ 500 per week. We decided to wait two-three weeks and, after that, conclude. You request income in 3 weeks and see that the first week's revenue is $ 50, the second is $ 60, and the third is $ 800. I knew it! — you think at this moment. If I were in your place, I would begin to doubt the correctness of this data. Perhaps there was a failure in the billing system? Maybe the one who collected the information for you today did not get enough sleep and put an extra zero by mistake. And if all the indicators are correct, then it is necessary to find the reason for such growth. There is a high probability that there are some factors that caused this splash. You can safely go to the managers and say — the revenue from this tariff is $ 300 per week. Still, this figure cannot be considered fair because there is a football championship in the country, and many tourists arrive in a couple of days and use SIM cards with this tariff plan. And if you exclude this week from the analysis, then the average revenue will be $ 55 per week.
Ambiguous indicators
There are cases when it is necessary to make an assessment, but there is no exact method from calculation and analysis. For example, we do not have single-valued KPIs for calculating employee performance in a sprint. One developer can close ten tickets in 40 hours, another in 45 hours will complete only three. But the problem consists not only of developers' work speed. Here it is necessary to take into account the complexity and volume of tickets. In such situations, you need to calculate the entire team's average performance and use it to conclude each employee's efficiency. Or, take the average figure of a particular employee and see if this is his standard time of work. You can not conclude with only time or quantity; you need to be more flexible and see all the factors.
For example, we're analyzing sprint planning results. What may be easier than data grouping from each developer out of the columns planned and spent? We compare data, the plan and fact practically coincide in each sprint, but suddenly, we see a big difference in one of the sprints. Does it mean that we planned the sprint poorly?
It is necessary to understand more carefully the reasons that led to such a gap. In sprint 19 (on the picture), we attracted interns to the project, and the developers began to work several times slower, distracted by them, while the number of hours spent increased. This indicator is typical under the existing conditions.
In conclusion, I would like to offer you, even though you
In conclusion, I would like to offer you, even though you
trust yourself more than anyone, to ask another person to look at your analysis result, especially if you are just a beginner analyst. It is always useful to get questions about data that you did not foresee, and they can lead you to a different point of view. Look at your analysis with a different look, do not make hasty conclusions, consider different versions.
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Worked on the article:
Juliana Amelina
Deputy Chief Executive Officer
