Insights — Data analytics done right

What is the purpose of Data Analytics?

Why are people obsessed with data? What makes data so important? The data is collected, cleaned, processed, analysed and interpreted in order to get insights. That sounds cool but what does it mean? Anything that helps us make the right decision can be considered an insight. It is the piece of information which influences our decision making process to do something in a way which will maximise the rewards of our decisions.

We look how many likes our photos on social media get, how many times our blog post is read, how much money we save each month. This is data. It provides us with insights: which photo should I pick for tinder to get a date, what topics should I focus my blog on, where can I save some money for that trip to Japan?

When we apply that on a larger scale / company scale — the main purpose of data analytics is to provide insights to the decision makers so they can make the vision of the company happen. It doesn’t matter how catchy your company’s motto is, the main goal is to make profit. Whether that profit is invested in constant improvements of the quality of the product/service it makes, developing the country it operates it, solving global problems like climate change, giving money to charity, keeping high salaries of its employees or financing the yachts and villas of the top management — is irrelevant.

Does this mean that most of the data analyst work is focused on figuring out how to increase the profit? Yes, it does. Optimising the product, increasing customer experience, increasing the loyalty of customers etc. are all tools which in the end serve the purpose to increase the profit. So whether you investigate how to get better reviews, how to make your customers return — the purpose of the data analytics is to provide valuable insights to the decision makers.

What do most of the data analyst positions look like?

Every person who has worked with data knows what reporting means. Collecting, transforming, storing, cleaning, calculating and visualising are part of that. Whether it is done in Excel, Google Sheets, SQL, Power BI, Tableau, Google Analytics, Google Data Studio, Looker, Metabase… The tool and the process varies yet the “reporting” still means the same: someone wants to measure this and that in such and such a way with such and such breakdowns repeatedly. The fact that you have automated the entire process instead of employing people to do it regularly is great, but does this give the insights needed? Does it fulfil the purpose of Data Analytics?

If all you did is defined some metrics to track (KPIs, OKRs or other fancy name) and tasked your analytics department with creating processes and places to monitor them, then I have a really bad message for you:

“You might just have missed the entire purpose of data analytics and to make matters worse, you might have demotivated a lot of the people who now do their job to make some numbers green.“

You might have taken the joy from designers and developers who work in the company to make a great product for customers or to solve some great issue like find the cure for cancer or bring people to Mars into increasing the “KPI 2.2.1: increase efficiency by 2% in Q3”. The above mentioned approach is pure focus on data without the love for it. It’s like paying for sex, it might fulfil your need now, but won’t bring you joy as having sex with someone you love would.

To make the above approach even more nonsensical you need to understand the following: You can lie with any metric and you can easily get fooled by it. The world is not simple. People are not simple. Decisions are not based on a single number. Companies do not thrive just by meeting some objectives. There isn’t a metric which is good when high and bad when low. Increasing the click through rate without increasing the conversion rate does not help. Increasing the conversion rate and decreasing the profitability per transaction might not help. Increasing profit today at the cost of lower retention rate may kill your company next year.

There is just too much to think about and take into consideration when looking into the data. Ensuring high data quality is a must. Otherwise we have nothing to discuss. Crap in the data means crappy insights which might mean harmful decisions. Once your data is clear, you still haven’t won the fight for meaningful insights. There are countless biases in our brains which might lead to incorrect interpretation of the data and thus again to shitty insights. To name a few: the simpson’s paradox, survivorship bias, availability bias, confirmation bias, voluntary response bias and we can go on and on and on…

What do fancy data analyst positions look like?

There are a lot of people who understand data and understand the role they play. If such people are in the leading position in the company hierarchy, they give enough focus to making sure the data is in sufficient volume and quality available to data analysts. They prioritise the quality of the data over making random noise on how to be data driven and how to visualise data so powerpoint slides make some manager happy. Then they hire data scientists, people who understand data and understand statistics; the logical, exact and precise people who are able to avoid the thinking biases mentioned in the previous paragraph.

The role of the data scientists is to make sure that what is done with the data makes sense. Whether we are talking about the interpretation of simple reporting metrics, the proper setup of an AB test on a website, building up a prediction model, creating customer segments or making buzzwords like machine learning bring higher profits, the main purpose of data scientists stays the same: make sure what is done with the data makes sense and brings relevant insights which the decision makers can trust.

I call this the fancy data analyst position yet in reality it is not that fancy. 95% of this work lies in cleaning data, changing company processes, explaining to people why 2+3=5 hundreds of times, testing, trying, failing, feeling misunderstood etc. Only the remaining 5% is implementing the model, showing beautiful visualisations and showcasing your superpowers and magic skills with data to people who probably have no idea what you are talking about but trust you anyways = fancy data analyst.

Do data analysts and fancy data analysts fulfil the purpose of data analytics? Do they bring valuable insights from data?

They kinda do and kinda do not. It all depends on how their work is managed from the beginning. Here are some yes or no questions to help with the answer:

Is the expected outcome clear to the decision maker?

Do the decision makers know what and why they need from a data analyst?

Do they understand what the newly hired machine learning expert should bring to the table?

Is the company’s product even ready for including some data driven personalisation of the product?

Do people in marketing know how to use the customer segmentation / personas done by k-means clustering?

Are the digital product designers aware of what the behaviour of the users on the website means and how they should re-design the product?

Are the decision makers aware of how the data insights should be reflected to beat up the competition?

All these questions need to have answers and a plan before a data scientist is hired. When visiting blogs, meetups, conferences and talking to data analysts/scientists, I hear very often that their work is not being used or it is being used in an incorrect way. I often see people who love data and love what they are doing but end up demotivated with company processes and being misunderstood by stakeholders when presenting their work. Very often it is not the fault of the data people. Why? Because most of the time the companies have no clear understanding and expectations from the project they set the data person to do.

On the other hand, everyone wants some data yet has no idea how he/she wants to use them. A lot of the time people think they understand each other: the data person explaining what stuff means, the listener then interpreting it in a totally different way. The data world is complex, it is very precise, exact yet at the same time it is never “clear”. Most of the time the results are probabilities and most of the time no one really understands what probability means and how to make a decision based on it.

How does a data analyst provide valuable insight?

The data analyst must put his/her work into a larger context. The data analyst is the person who says: “NO, we should not be happy with Conversion Rate increase as we are starting to sell more of the low margin products and much less high margin products with the recent changes. We are also selling more of the cheaper products as the result of misleading advertising and people are not satisfied with that part of our product and do not return. Even though we are making more profit now, we started to lose loyal customers. In the long run it is not sustainable for our current company vision and will make us uncompetitive in a year or two.” Yet in the complex world we live in, this is still not enough.

The data analyst must realise he/she is seeing only the quantitative part of the picture. More people visiting our page might not mean more people interested in our product. What if the purpose of the visit is to find contact details and call customer support? More people coming to the web thanks to a new marketing campaign leading to more purchases might not be a way to congratulate marketing people right away. What if the campaign set higher expectations for the customer which were not met? If the same customer did not come from the ad he/she might have been satisfied with the product. Yet he/she came from an ad promising a paradise and he/she got an okayish experience. In the end this made him consider the company as a shitty service and he/she will not purchase with the company again.

What can be done about it? How can those raw data and numbers describing how something performed be transformed into insights then? By putting the data into context, by giving those data a meaning a real-world interpretation and context. How can that be achieved? By combining the quantitative world of a data analyst with the qualitative world of a UX researcher, a designer, a marketing specialist, a market researcher, a customer support employee etc. The data analyst should make sure that he/she can link the raw quantitative world into what the customer feels when using the product/service. It is not about how many times we log XY customer’s action. It is about understanding why and when log XY is triggered by a customer.

“A customer opening your website during his travels probably doesn’t care about the company increasing the pageviews by 4% month to month. The customer probably has an issue with your product and has a problem and is looking for a solution. It is not more pageviews as a result of more people wanting to leave their money with you. It is a customer who needs help and if you don’t understand it, you already have lost him/her.”

How to make data analytics great again?

1. Understand the purpose of data analytics and make sure it is being met
2. Always focus on data quality, without clean data any investment into reporting/analytics is wasted money
3. Hire people who understand the data and avoid thinking biases. You don’t need people who tell you everything is great, look we have more pageviews and higher conversion rate.
4. Make sure you have clear expectations and you know how you will use the outcomes of the work data people are doing
5. If the data analyst doesn’t understand the relationships between multiple areas and departments of the company — make him/her understand it. The word analytics by itself points in the direction to be able to find relationships between stuff, that’s what the person needs to see and point out!
6. The data analyst should work in close cooperation with qualitative researchers like UX, design, market research and be aware of how customers perceive the product. Going through specific customer cases with the customer support might give a new context to the data he/she is analysing.
7. Only after all the above has been done, you should let the analyst show the insights to product managers, business people and other decision makers. Showing incorrect data to decision makers does more harm than good. When the insights brought by the analyst can be trusted, make him/her part of the decision making. Seeing how the decisions are done and how the people think makes sure the analyst knows what kind of insights are valuable for future projects.

All the pictures shown in the article are from www.dilbert.com by Scott Adams.