If you work in marketing, sales, finance, or operations, you probably notice data creeping into your day-to-life. And with good reason: businesses that run on data are more successful, reporting 33% higher revenue and 12x revenue growth. Problem is, in order for data to get these big results, it has to get out of the hands of IT and into the hands of people who need it to make decisions every day — that’s you.

The good news is this, you don’t need to be a data analyst to get results from your data and start making data-driven decisions. In this post, we’ll lay out the different types of tools that you have at your disposal, and break down what they do, what they do well, and what they don’t do so well.



Spreadsheets are hands-down the best-known data tool. They’re fast, easy and amazing for certain tasks. The flip-side is that because of their popularity, they’re often misused for things they’re not good at.

Where spreadsheets shine:

  • Financial Modeling. Spreadsheets are great for the kind of assumptions and testing needed to put together month-by-month forecasts of financial performance.
  • Brainstorming. Spreadsheets are fast and easy: perfect for back-of-the-envelope calculations or preliminary work on a new data set.
  • One-Time Analysis. Spreadsheets are great tools for one-off investigations. Grab your source data, analyze, and draw conclusions quickly.

Where they fall short:

  • Operational Reporting. Spreadsheets don’t automatically update. This forces decision-makers to use stale information, and wastes your time manually updating and maintaining reports.
  • Complex Analysis. Every layer of complexity increases the potential for error.
  • Sharing and Collaboration. Spreadsheets offer little insight on what numbers mean or where they came from. Once multiple people are collaborating, the risk of mistakes skyrockets.

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Mistakes are incredibly easy to make in spreadsheets, but difficult to spot. It’s estimated that 88% of the world’s spreadsheets contain errors. This is a big deal, because an error in a single formula can flow through to other unsuspecting cells. Proceed with caution!

It’s estimated that 88% of the world’s spreadsheets contain errors.



Structured Language Query (SQL) is the (nearly) universal language used to interact with databases and query large data sets. Because it’s language, not a program, you’ll need a SQL client to be able to write queries. SQL excels at one-time analysis, and its English-like syntax makes it more accessible to non-technical people.

Where SQL shines:

  • One-time Analysis. For quick and dirty answers, there’s nothing quite as effective as writing some SQL and exporting the data to a spreadsheet or statistical tool for further analysis.
  • Basic Data Sleuthing. SQL is great for basic questions like “How many orders did we have yesterday?” or “How many active customers do we have?”

Where it falls short:

  • Operational Reporting. Most effective operational reports require complicated SQL statements that are difficult to write, debug, and maintain. It can be extremely frustrating!
  • On Production Servers. Running SQL queries against your production database is an absolute no-no. A long-running SQL statement can cause your website to grind to a halt.

As the complexity of a query increases, SQL becomes harder to write, debug, and maintain. Complexity also makes execution take longer. If you’re running complex queries against large data sets, expect to wait quite a bit for the results.

Statistical Software


Most business reports rely on sums, averages, and counts. But if you need to do something more analytically rigorous you’ll need specialized software. A spreadsheet will tell you your sales for last quarter; a statistical tool will help you determine the effects of a branding campaign on your sales.

Where statistical software shines:

  • Discovering Causality. If you want to answer questions like “What characteristics predict purchase?” and “What behaviors predict churn?”, you need to use statistical methods and tools. Attempting to answer questions like these without statistical rigor can set your business on the wrong path.
  • Deciding Between Competing Options. Form a hypothesis, collect data, and then accept or reject it. This process is commonly applied by digital marketers doing A/B testing, a simple (but incredibly effective!) use of statistics.
  • Advanced Market Segmentation. Take what you know about your customers and cluster them into segments so you can develop targeted products.

Where it falls short:

  • If You’re a Novice. Incorrectly run tests will lead you astray. If you don’t have the background to confidently run a statistical test in a tool like R, STATA, or SPSS, it’s better to stick to something more familiar.
  • Post-Hypothesis. You must create your hypothesis prior to conducting a test. If not, you run the risk of cherry picking results that support your preferred outcome.
  • When an Easier Tool Will Suffice. Statistical tools are the hardest of all a business user’s tools to master. If an easier tool will suffice, use it. The point of data analysis isn’t to prove your analytical muscle, it’s to drive business decisions.

For strategic decisions, be rigorous in your analytics. For day-to-day work, getting to a decision is more important than flexing your analytical muscle.

Visualization and Dashboarding


Visualization and dashboarding tools provide a graphical layer on top of your data. They shine when you’re trying to tell a story with data. These types of tools encompass everything from chart building tools and interactive visualizations to plug-and-play dashboards.

Where visualization and dashboarding shine:

  • Complex Stories. Some data is inherently complicated, and advanced visualization techniques can help you build a comprehensive story.
  • Making an Impact. Most of time you can get by with basic tools like line and bar graphs, but when you need to make a big impact fast, use data visualization tools.
  • Showing the World. Dashboards excel at keeping critical metrics in your line of sight at all times, helping every member of your team stay focused on the numbers.

Where they fall short:

  • When it’s Unnecessary. If you can just as easily accomplish what you’re looking for with a different tool, stick with that. No need to add unnecessary tools to the mix.
  • Number-Crunching. Visualization and dashboarding tools do not perform data analysis—they provide a lens to look at data that you’ve already analyzed. If you need to analyze data, do it in another tool.

Know what you’re paying for. Visualization and dashboarding tools can sometimes look very similar to business intelligence tools, but they’re out to solve very different problems. The key difference is that visualization and dashboarding tools are an overlay of your data (front-end), while BI tools allow you to manipulate your data in a data warehouse (back-end).

Analytics Tools


Google Analytics is the most well-known analytics tool. But today, just about every piece of business software includes an analytical component. Tools like Silverpop, Hootsuite, and AdRoll all provide analytics related to their product. The basic reports that typically come standard make it easy to answer single-domain decisions like “How many website visitors did I get?” or “What subject line performed best?”

Where analytics tools shine:

  • Data Collection. Analytics tools often collect the data that they analyze. If you need to gather the data, don’t do it yourself—get a tool!
  • Single-Domain Decisions. Analytics tools are purpose built. If you want to know how to optimize your website or email marketing, an analytics tool is your best bet.
  • Answer Common Questions. An analytics tool is built to answer a specific set of commonly-asked questions, like: “How many website visitors did I get and where did they come from?” “What subject line performed best?”

Where they fall short:

  • Cross-Domain Decisions. Most business decisions require data from multiple data sources. For example, optimizing your product prices means you need to integrate your web, order, and accounting data and start running experiments. Analytics tools can’t do this.
  • Answer Unusual Questions. Analytics tools ultimately they force you into one particular way of looking at the world. If you want to look at revenue excluding sales tax but your analytics tool doesn’t have that option, you’re out of luck.

Analytics tools excel at collecting data related to their domain and showing it via simple, templated reports.The growth in the analytics space has created a lot of data, but has left many business users still looking for answers — companies cannot run on analytics tools alone.



After evaluating the strengths and weaknesses of these 5 tools, we built RJMetrics, a complete analytics platform that allows you to find the answers you need without digging around in spreadsheets, databases, and disparate analytics tools.

Real insights begin when data works together. To this end, RJMetrics extracts raw data from each of your transactional systems and loads it into a data warehouse. We then prepare your data for analysis, and serve it to you with a visualization layer including customizable dashboards. We can take data from any source, and analyze however much of it you have.

Where RJMetrics shines:

  • Operational reporting. RJMetrics automatically replicates your data from where it lives, so that it’s always up-to-date. With RJMetrics, you no longer have to waste days updating static reports!
  • Data exploration. With RJMetrics, all of your data lives in a single data warehouse. This means you can easily explore any aspect of your data seamlessly. Ask any question about your business and get an answer back immediately.
  • Collaboration. With traditional tools, it’s hard to get everyone in your organization on the same page. RJMetrics allows you to share data with your whole team. Publish a finished dashboard, or let others slice and dice the data themselves.
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When you probably want to choose another tool:
  • Brainstorming. RJMetrics excels at analyzing actual data, not hypothetical scenarios. You should use a different tool for those back-of-the-envelope estimates.
  • Predicting the future. You should turn to statistical analysis tools like R, STATA, or SPSS for most of your forecasting needs.

Before RJMetrics, only the Fortune 1000 had access to a complete analytics platform. The technology was complicated and heavyweight and, as a result, tools took lots of time and money to implement. Today, you can get up and running with your own custom RJMetrics implementation in a week.

What this means for you

The business world is changing. A proliferation of data, and the tools to analyze it, necessitate that business users learn to think in data. Like anything new, this is a little scary, but incorporating data actually makes marketing a lot more fun. Data allows you to confidently take risks and test new ideas, freeing you to be creative and to innovate.

Want to learn more about the data tools you have at your disposal?


Today, data visualizations are everywhere. You can find data visualizations for almost anything: Game of Thrones character arcs, How Americans Die, baseball, and whether April showers really bring May flowers. This wasn’t always the case. One of the pioneers of data visualization was Florence Nightingale. While remembered for her nursing abilities, she was an important statistician, with data visualization skills that inspired massive social change.

Screen Shot 2014-04-25 at 2.42.34 PMIn her day, Florence Nightingale was revered. Queen Victoria famously wanted her for the Cabinet, saying of her, “Such a head! I wish we had her at the War Office.” She was a fighter, arguing for what she believed in and forcing the world to change when made to understand the facts she presented. Since then, her role as a fierce data journalist, feminist, and war nurse has been obscured by a fuzzier vision of the Lady with the Lamp.

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You might have heard or seen by now that we are in the middle of releasing major changes to your RJMetrics dashboards. The most obvious changes were made on chart visualizations, but this is just the tip of the iceberg. We will also be introducing numerous enhancements that aim to improve your overall user experience. At the time of writing this blog post, we have rolled out the new features to 35% of our clients, and we continue to migrate new clients every day. So if you haven’t yet been transitioned, rest assured that it is just a matter of time for the new charts to reach your dashboard.

In this blog post I will explain what the changes are, and most importantly their benefits to you.

New Dashboard Visualizations

The new charts will now look much more vibrant. They will be rendered using the HTML5 standard which will allow faster rendering, and multi-platform support. The library that we are using also supports a plethora of other types of chart visualizations, which will allow us to extend our chart type offerings in the future.

All new charts will also be using the Scalar Vector Graphics (SVG) format which will allow you to download a graph and resize it without any loss of image quality. Another cool feature included in the new HTML5 charts is the ability to directly click on the name of a series in the chart’s legend to remove or add the series from a multi-series chart.

Transitioning away from Flash means that you can now actively view your dashboards on your iPad/iPhone. Note however that chart editing is not yet fully supported on your mobile devices.

Faster Chart Loading

Chart loading times are dramatically faster with the new chart system. This is partly due to HTML5 rendering, but it is also due to the introduction of a redesigned caching framework. For non-technical folks, caching is a method of saving data in memory so that future access of the same data is faster. More specifically, we use caching to store chart data for faster retrieval. Our live production testing revealed a 10-fold loading time reduction for new charts compared to old charts. The benefit can be even more pronounced if you have charts with a very high number of data points, such as our advanced cohort analysis charts.

Faster Updates

The new caching system will not only reduce chart loading times, but also reduce the time taken to complete a data update cycles (syncing your RJMetrics data warehouse with your own database). This is a result of speeding up the chart pre-caching section of an update cycle.

Another indirect performance enhancement will come from the fact that we will no longer need to make certain calculations during an update cycle. These calculation processes were integral to the proper functioning of the old chart engine, but we have eliminated this requirement in the new charts.


We are all very excited about these changes, and we all believe that this is a major step in the right direction for user experience. The most noticeable change will be chart rendering, but under the hood, these changes will enable us to iterate faster with new features and improvements.

As always we really value your opinion, so let us know what you think about the changes by dropping us an email at