Data at Your Fingertips: How to Design a Dashboard

Data dashboards are a way to represent large amounts of data in a condensed and visual manner. A dashboard typically consists of widgets or reportlets. Each of widgets includes one or several KPIs, supplemented by graphs, small data tables, etc.

If you are supposed to prepare a concept for a dashboard, the first task is to find out the stakeholders’ requirements. Who are you building the dashboard for? What data do they need to see, what insights do they want to gain and how detailed should the information be? E.g.  the dashboard for the marketing department will substantially differ from the dashboard you are building for the CEO of the company.

Secondly, prepare a rough draft of the dashboard layout and the widgets it will have. Try not to include more than 10-12 widgets, otherwise your dashboard may become too cumbersome to view and understand. This is also where you define what KPIs the dashboard will show.

Rough draft of a dashboard.
Rough draft of a dashboard.

Thirdly, decide what time period will be included. The majority of dashboards will show only today’s or this week’s data. However, if the website has significant monthly or daily fluctuations in performance, you may want to include a more extended period of time. If stakeholders want to use the dashboard for performance monitoring, the data naturally has to be real-time or with a minimum time lag.

In the fourth place, consider how to represent the data you will gather. A widget may contain a number, a graph, a data table, or a combination of these. The graphical representations of numbers is the most visual way that allows to grasp the meaning of data within seconds. Choose the type of graphics wisely: a line graph will show the development of KPIs in time, the pie chart will show what percentage each segment or product contributed to the total and a bar chart is a good way to visualize and compare several dimensions. The picture below shows how different data may be visualized. In addition, you can use scattergrams, process visualizations, bubble diagrams, etc. In any case, make sure that your graphs are not cluttered and convey a meaningful story.

graphics on a dashboard
Examples of graphics.

numberIf you choose to use a data table in your widget, select only top 5 entries and do not include more than three  columns. In addition, do not be afraid to put a single number in your widget, in case this number is important.

The next decision is what technology you will use. The majority of modern analytics systems include a dashboard feature. If the feature is not sufficient for your requirements or is missing altogether, you may consider using specialized data visualization software (in fact, even Excel offers dashboard building). The best solutions will be those that allow for automatic export and processing of data, without much manual work. Ideally, you should be able to pull data from several sources – e.g. from the website itself, from the order processing system and from social media.

Also think how you will share the dashboard with stakeholders and if the system provides you with this option. Some will prefer viewing the real-time data, others will be satisfied with a weekly/monthly report in PDF format.

And finally, create a visual mock-up of your dashboard for the stakeholders before starting to implement your concept.

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Common Reasons for Incorrect Data in Web Analytics

Incorrect data is an acute problem for online businesses. While most businesses have started to realize the importance of data-mining and performance-based marketing, it seems that some of the effort has been going in the wrong direction. The point is not about having as much data as possible, but rather having the data you can actually work with – structured, complete and correct.

Below I will outline some common sources of erroneous or incomplete data and give some advice on how to avoid the mistakes.

Unclear or imprecise definitions of KPIs.

Even if your company is not very large, make sure that everyone dealing with data understands precisely what is included in statistics and how it is calculated. It even makes sense to create a written document defining the KPIs, no matter how simple or self-explanatory they might seem.

For example, if you want to calculate CTR (click through rate) for banner advertising, which is, in essence, the number of clicks on the banner divided by the number of banner impressions, you can ask yourself a range of questions. Do you want to apply unique banner impressions and unique clicks (that is, per user)? At what intervals do you want to measure the CTR (per hour, per day, per week)? How do you go about natural fluctuations in CTR (e.g. during the day vs at night), do you want to make them part of your statistics or just ignore them and take the average? How do you group the data: by region, by traffic source, by banner type, etc.? If you run banner tests, do you want to exclude the test data from the overall statistics?

As you can see, the answers to these questions might greatly influence the outcome, i.e. the CTR your  data analyst will produce at the end of the day.

Technical issues

The more complex a system is, the more likely it is to malfunction. Always check for technical issues if dealing with data inconsistency. For this, it is best to work with statistical and technical benchmarks, based on the normal system behavior in the past.

Technical problems influencing the data consistency may, firstly, be caused by the under-performance of the system itself (for example, the banners are not served for a period of time, or are not displayed correctly in some browsers). Secondly, even if the system functions well, there might be problems in capturing or storing the data (e.g. not enough RAM to perform operations causes the database server to crash). And thirdly, if you do not query the database directly but let the data flow through a business intelligence system, there might be all kinds of compatibility problems between the systems.

Incorrect calculations

This is the least predictable source of data inaccuracy. It starts with how the data is collected and aggregated in the system. Even if one has enough clarity on how the KPIs are constructed, there is always a chance that the setup of the analytics system will divert from the desired parameters. Furthermore, if data processing and analysis are largely done manually, the probability of a mistake rises with every step. Also, merging the data from several sources can impact the data consistency in a negative way, especially if some parameters have to be converted or rounded up before the merging.

Thus, human factor should not be underestimated, and the only way to minimize the number of errors is double checking the calculations, or better still, automating as much of data processing as possible.

In conclusion, if you find that the data does not “seem right”, even after you have excluded every possibility of a mistake, you might need to look for the reason outside of your analytics system. One of my previous blog posts looks at this issue in more detail.

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Will Market Research Survive?

No, I do not want to make a prediction about the death of market research as such, but rather some of its forms that have traditionally been the “cash cows” of large market research companies.

Take, for example, panel research. The very essence of retail panel research is being ruined by the growth of e-commerce. Measuring at the point of sales is becoming more complicated now.  Who can possibly register the flow of goods from numerous on-line shops, especially those outside the country? There is a missing link there, and the gap is growing.

Another area which is unlikely to survive very long is test market with measuring advertising response.  As online marketing budgets are growing and the advertising shifts from TV and radio to the Internet, the companies feel more empowered to track their own advertising campaigns and optimize them as they please.

Even in qualitative research, traditional focus groups may, to a large extent, be replaced by scanning online forums and social media for new ideas or suggestions for improvement. Moreover, the data are available globally and in real time at no extra cost!

And last but not least, desk research has become increasingly simplified through the  use online search engines and other digital data mining tools. Possibly,  in some years, complete market research reports which normally took months to create and used to cost thousands of dollars will be created in a few mouse-clicks using special software.

Think of the new World 2.0 as an interlaced, data-overflown place, where the consumers and whole markets are getting more and more transparent, with or without professional market research as we know it.  Shifting strategic weights and entering new fields of play will probably be the biggest challenge for market research companies in the years to come.

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