Dashboards are a Waste of Time and Money

Dashboards are often waste of everyone’s time and money -- and so are the companies that sell them. They’re not a strategy.

Having done the “Big Data” thing for about a decade, I’ve seen so many whitepapers and projects tell this story: “They had a lot of data. Then they put it (on a cluster/in memory). The executive got a faster dashboard. The End”.

Visualization is a means, not the goal. Your goal should be to make more money. If you have engineers/analysts whose goal is to make a dashboard … they’re not making you money.

Instead, you need to write software. The software turns the data into processes that drive revenue.


Dashboards and reports require human process changes to impact an org. Change requires emails, meetings, documentation, negotiation, commitment, and mental overhead.

Here’s a story:

We (RoboticProfit) were consulting for a nonprofit that brought hundreds of millions of dollars of prescription drugs (and several hundred varieties). They were so huge that buying too much of one drug increased its price on the market, due to scarcity. They also got discounts on 100 drugs. So they needed to figure out when to buy what drugs at what price.

Their current process was, like many businesses, a spreadsheet and prayer. They would look at the drug prices for the last month, and then guess what would be the best way to apply discounts, and submit their buys.

They knew this was inefficient, so they contacted us to fix it. Their request was to get all the pricing history data and “make some dashboards”. We did that and presented the fun, colorful pricing graphs to the executive team. They were updated in real-time as drug purchases were made. The execs loved it, and said “great! Now that we can see all the data, we can from a Pricing Team to figure out our pricing strategy”.

Oh, great.

We checked in a few months later to see how that was going. As expected, there had been dozens of meetings and even more spreadsheets and even some process flowcharts of who was responsible for which parts of the pricing decisions.

But all they were doing was looking at some numbers, and guessing how to change them into other numbers. Computers are way better at that.

We helped them get out of this rut by building a market simulator -- given the entire market history, we could predict what purchases should be made at which price in order to optimize the spending. This simulator output a list of drugs, volume, and pricing. With human approval, the purchases were made electronically.

Using data science let us prevent yet another team of 6 people manually making sub-optimal decisions.

So when you want a dashboard, instead ask “What decisions could I make with this data?” and write the software to make those decisions, instead.

Making $47,000,000 with data ain't easy. But you can do it.

Turn Your Data Into Profit

“‘The amount of data the system can handle and the performance is fantastic.”

“Data is an enabler. … We can change the product we put in front of the customer.”

“We don’t think in terms of databases anymore, we think about what we want to achieve with the data.”

How would you like to be able to say things like this about your data?

The quotes above are from HolidayExtras CEO Matthew Pack. His company is on track to make $47,000,000 more in a year. Not because of gimmicky marketing or random chance: they’re using their own data to sell more to their best customers.

(get the whitepaper to learn how we did it)

Like many established companies, HX entered the digital market in the early 2000s. They knew they should be collecting data, so they collected data. A lot of it. But they didn’t have a data strategy or supportive data infrastructure. 

Of course, data accumulates rapidly. It just keeps growing, year after year, becoming irreplaceable and opaque.  

What HX needed was real-time streaming, on-demand analytics. They needed the ability to write business problems in code. They needed the right data architecture.

Fundamentally, Big Data represents a technical fix to a business problem. But you have to understand your business problems before you can figure out the right technical fix. Without KPIs and revenue goals, any new Data Platform will lack a driving purpose.

The first step you need to take when creating a new Data Strategy is to carry out a Data Diagnosis: observe the landscape and determine the context of the data problems.

Talk to all your executives, engineers, and other stakeholders. Get people out of their silos. Look at the systems architecture, code, and raw data. Understanding the different data silos and the paths data takes through the system.

Here are some key questions to ask:

  • What data do you collect?

  • Where does that data live?

  • What do you want to do with data?

  • What is your current infrastructure?

  • How did it get this way?

  • Who ‘owns’ the data?

  • Who can actually use the data?

  • How have you attempted to fix your data problems?

  • What are your KPIs and business goals for this year?

  • How do you measure improvement?

  • How would you use data in a dream scenario?


Your key data-supported business metrics might look like this:

  1. Evaluate campaign ROI

  2. Understand response rate

  3. Visualize marketing reach

  4. Understand how to increase customer retention rate

  5. Increase customer value

  6. Cross-sell to existing customers

  7. Migrate customers to more valuable segments


Once you know what you want to do with your data, you can start looking at the big challenges of a Data Strategy:

  • Collecting the right data

  • How to apply Machine Learning and Data Science

  • Integrating multiple data sources

  • Selecting the right architecture

  • Building out the right infrastructure




A Big Win for HX: E-mail Marketing

HX was sending the same sales emails to the same customers at the same time: spring breakers looking for cheap adventure got the same pitches as business people planning their weekly trips between London and Berlin. They knew that if they could understand and response to these different market segments, their sales would go up.

Enhanced email marketing requires a data platform that is easy to collaborate on, real-time, and painless to operate in production. Functionality includes:

  1. Real-time / stream processing at large scale (thousands of events a second)

  2. Storage of this data so that it can be accessed and queried at any time

  3. A simple way to transform and manipulate the data in batch or streaming mode

  4. Data feeds anyone can subscribe to without being a data engineer

  5. Scalable backend so no new operations hires would be needed

  6. Output to an e-mail marketing system to implement features such as automatically e-mailing abandoned carts

  7. Machine-learning based customer segmentation for hyper-targeting of campaigns


For HX, the solution ended up being drastically different than their current architecture—hosted in Google instead of AWS, streaming instead of batch, and Python/Java instead of SQL.

The core requirement was easy-to-use stream processing that non-engineers could use. Nothing like this existed in the market, so RoboticProfit created the solution from custom software and cloud services.

Get more details on how RoboticProfit led HX through their data discovery process, developed a data roadmap, and built out their platform by downloading our whitepaper.


HX is currently using their new, custom data platform in production to feed an ambitious new email campaign system. Instead of just e-mailing millions of addresses every two weeks, the platform uses data to target those customers and send them what they’re likely to buy, based on their past behavior. With a fully personalized and data-powered email, add-on, and product discovery platform, industry studies indicate that HX will see $47,000,000 in additional revenue in the next year.

Are you ready to use your data? Schedule a consultation call