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Keep asking 'why?'

Exploring the first 90 minutes, 60 hours and 30 days of

adopting data visualization.

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And buried deep in this data, if you know where to look, are potentially transformative insights that could help companies make smarter decisions and develop better products, faster; create amazing customer experiences or invent completely new services.

The mass digitization of products and services, and even entire business models, has unleashed a tsunami of data on most businesses, large and small.

Introduction

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Inevitably, a whole host of software solutions has sprung up to satisfy this thirst for all things data, and ‘data scientist’ was even touted as the sexiest job in the 21st Century1. (And about time too, if you ask us.)

That means that, in just a few years, being able to capture, analyze and interpret that data has gone from ‘gee, that would be great’ to ‘I needed those numbers on my desk yesterday!’.

Introduction

1 Harvard Business Review

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Gartner predicted that by 2017 the number of so called ‘citizen data scientists’ (business users, rather than data specialists) will grow five times faster than the number of highly skilled data scientists.

But really, you don’t need to be a pin-up with a PhD to make data start working for you. The prevailing trend among makers of data analytics tools (you might also hear them called business intelligence tools, or BI tools) is actually to take the science out of preparing and analyzing data.

Introduction

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With a better understanding of your data you can take the speculation and intuition out of decision making and become a smarter business. And you can keep asking ‘why?’ until you get the information you need.

This short guide is designed to show anyone just starting out on this exciting journey that, even when starting small, a quick time-to-value is achievable with the latest data analytics tools.

Introduction

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The first 90 minutes

It still amazes us that what used to take days in Excel can now be done in less than an hour.

Nuno NetoManager of Planning and Control, Portugal Telecom

Chapter 1

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Ten years ago, data analytics was for serious players only.

The tools were heavy and expensive, and the science was obscure, so before companies could invest there would have to be a rigorous justification process and some expensive recruitment.

IT would need to become your new best friend, too – it would take months or years to get any business insights out of corporate data, the process was error-prone, and the results were often stale. Plus, you would get a static, non-interactive report after the decision was needed.

The first 90 minutes

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But fast forward to today and think about the way we use technology.

As consumers, the apps on our smartphones or tablets are easy come, easy go. If they work, then they stick. But if they don’t, a casual swipe of the index finger and they’re gone. Why should the tech we work with be any different?

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The first 90 minutes

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Answer: it shouldn’t. This line of thinking has led to analytics solutions where the software does most of the hard work...

...like easily connecting and preparing data and machine learning techniques to automatically identify the best metric or visualization for particular data sets.

For example, a user loads three different csv files and the analytics tool notices that there is a data and time stamp for each transaction. The tool smartly notices that the date field is consistent across all three sets of data and recommends a time-series chart be used to show sales performance over time.

The first 90 minutes

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There might also be a data scientist working somewhere to create and manage more complex analytics applications for the company, but the goal is still to make these available to business users so they can focus on solving real business problems, not technical data challenges.

It can also provide a simple click-through of each metric to help the user see the underlying details. Interactive analysis at the speed of thought gives any user the ability to ask questions and get answers, quickly.

By creating a self-service experience at the front-end, non-specialists can use data analytics to ask and answer questions, with little or no specialist support.

The first 90 minutes

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So, in your first 90 minutes with your new data analytics tool, IT can go out to lunch while you spend some time finding meaningful business insights – in seconds.

Chuck in a data set you use frequently, like weekly sales figures or registration numbers, and have a go at creating a dashboard to see what’s interesting. And when that’s done, change a couple filters of your dashboard and see what it tells you. Self-service analytics is iterative: it is trial and error, so it allows you to explore and find the unexpected.

The first 90 minutes

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For example, your marketing team has budget to host a 10-city tour to meet with prospective and current customers. A dashboard can quickly display the top 10 cities by customer count. But let’s say there is a 3-way tie for the final city because they have the same number of customers.

In a few clicks, your analysis can show which city has the most revenue opportunity or, if your tool has built-in GeoAnalytics, you can click and see the proximity of your customers to the city centers to determine the likely location of the event.

The first 90 minutes

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Is the data you’re working with correct? Spend at least a few of your first 90 minutes checking you’re working with the right numbers. That’s an early lesson you don’t want to learn the hard way. Perfection doesn’t exist in BI, but having confidence in your numbers can’t be underestimated.

The first 90 minutes

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It’s tempting, but creating pretty visualizations for anything and everything opens the door to ‘analysis paralysis’.

Avoiding this productivity-squashing condition is mostly about developing the right way of thinking. What, exactly, are you trying to discover? How will it help you add business value? Are you trying to improve a process or understand it?Keeping your objective clear in your mind will help to keep you and your team from chasing shadows.

Remember though, just because you can analyze and visualize data doesn’t necessarily mean you should.

The first 90 minutes

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Analysis paralysis or paralysis by analysis is the state of over-analyzing (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome.Wikipedia

Primary symptom: an acute sensation of what-did-I-come-in-here-for-again? Only known cure: ripping everything up and starting again.The data analytics school of hard knocks

The first 90 minutes

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1. StopKnow what you’re looking for. You can toil away for hours with data visualizations, generating acres of charts while getting nowhere. It’s fine to start with one question and then realize you should’ve been asking something different – that’s called experience – but whatever you do, don’t start without a clear goal.

Use data visualization responsibly! 3 steps to follow

The first 90 minutes

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Use data visualization responsibly! 3 steps to follow

The first 90 minutes

2. ThinkIf you find something, what are you going to do about it? Can team members also interact with the data to test or build on your findings? Is the business even in a position to act on your insights?

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Use data visualization responsibly! 3 steps to follow

The first 90 minutes

3. CollaborateYou don’t need a fully mapped-out strategy, but if you have found an insight you think is impactful, share them with colleagues through collaborative analysis tools such as sticky notes and comments that engage your team.

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The first 60 hours

Chapter 2

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In the first 90 minutes, we successfully loaded data, visualized it, and asked and answered some questions.

We got meaningful business results in just a few minutes, all on our own, without support from IT. It was fun, to analyze our sales numbers in a completely new light, prompting new questions and tackling them without extra scripts or coding.

But individuals using visual analytics for local insights is just the beginning.

The first 60 hours

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In your first 60 hours, we deal with the nitty gritty aspects of dealing with messy data, and helping you find deeper insights and share those with your team though analytical dashboards and applications.

The first 60 hours

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Your visual analytics tool can help you solve the problem of bad data with self-service data preparation and data wrangling.

For example, let’s say your sales data from a partner is missing profit for a particular quarter, or they use a different spelling of products, or use product IDs instead of product description.Your analysis tool should be able to visually help you merge, rename, group, or exclude data as you go.

Dealing with Messy Data and Data Wrangling

Garbage in, Garbage out. Bad data leads to bad insights.

The first 60 hours

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The first 60 hours

Each change to the source data should also be tracked for later re-use or to help you and others remember what you changed.

You should aim to have a repeatable process for getting the data, cleaning it and getting it into the system.

Heads upIf a solution is truly self-service then it should be able to take care of a lot of the preparation and cleaning process for you. We call it data wrangling – rounding up the useful information and discarding the superfluous in order to quicken the time-to-insight.

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A well-designed dashboard is an important factor in successful data analysis project. Here are some great interactive examples of best practices, based on actual data. Some of them are interactive too, so you can get a real sense of how data visualization works.

Broadcast Your Great Ideas

The power of the dashboard

The first 60 hours

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The first 30 days (and beyond)

More users, more questions, more insights

Chapter 3

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As your data visualization journey passes the 30-day mark, you’ll likely try to tackle harder questions and challenges of growing size and importance.

Your analytics tool should guide you, and not force you to do it alone.

Predicting an outcome with relative certainty is considered the “holy grail” for analytic experts. Years ago, the only people who could predict an outcome with data were statisticians and math geeks hiding in secret locations.

The first 30 days

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Sales volume data by region for given time periods is useful for showing performance trends, for example...

...but imagine if you could combine it with marketing campaign data – such as what promotions were running, when where and for how long.

The first 30 days

Today’s analytic tools can do the hard work for you. With built-in statistics and regression models, non-experts can see what may lie ahead, all in a single click. Modern tools no longer force users to write code to find deeper insights – they guide rather than leave you in the dark.

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That context adds a whole new dimension to your sales data, and gives both sales and marketing something really powerful to work with.

"We were looking for the ability to bring together lots of different information and present it in a format where it was easy for all of our end-users to access in one centralized place."Matt Samost Business analyst at the Tampa Bay Lightning NHL team

The first 30 days

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Twenty people all making the same dashboard would be bad for your blood pressure.

But bringing more users into your analytics processes, and allowing them to blend and mash-up the data, can quickly lead to far-reaching analyses, asking questions you wouldn’t have dreamed of asking a month ago.

To make this happen, consider how to create a self-service process whereby people can build their own dashboards. Practically speaking, what might this look like? Perhaps a log in portal or a shared series of dashboards – but who can see and do what, and why?

The first 30 days

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Think about what would best suit your processes, your data security requirements, and what platforms or systems you have in house that might help you roll something like this out.

The first 30 days

At 30 days and beyond,the kind of analytics tool you selected at the beginning starts to become really important.

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Understanding your ambitions for data analytics before you start can be hard. Do you want to blend more data, analyze data geographically, collaborate with your team, or predict an outcome? The foundation of all these questions is choosing a tool that can provide the breadth and simplicity for your analytics journey.

You need a solution that can grow with you. Organizations that are successful with data analytics and visualization, with established processes and well socialized insights, will always have a spectrum of different people and requirements for visual analytics.

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The first 30 days

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An executive just needs to know that it works, not how it works, and have easy access to the insights.

A data scientist will want the freedom to use the tools they already have, but be able to easily plug those results into a product that business users can access and use too. Differentiation among the various data discovery tools on the market is found in how well they are designed for the growing analytics needs of a company.

The first 30 days

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Many tools let you quickly design interactive dashboards, but become increasingly complex when asked to support harder-to-find insights, manage messy data from multiple sources, or support the needs of data scientists.

Everyone’s analysis project is different, so you need a reactive solution – one tool that everyone can use is the nirvana of business intelligence.

The first 30 days

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For companies exploring data analytics tools for the first time, or looking at upgrading their solution, it’s not really a case of ‘how?’ anymore, but ‘what if?’.

The technology might be easily accessible and simple to use, but what happens if you find something – are you set up to take action on it? Rapid innovation in decision-making is a key competitive advantage driven by the influx of data. Data-driven decisions are no longer a by-product of doing business. It’s the key to being in business.

Afterword: The power of ‘why’, and the pressure of ‘what if?’

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Hit the launch button on your data driven transformation with a no-cost trial of TIBCO Spotfire.

Start trial now

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It starts today

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Want to discover more about how data analytics and visualization can make data-driven decision making an everyday occurance for your company?

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Get the research

Research from the Harvard Business Review

The vast majority of business managers surveyed by Harvard Business Review said that despite deploying self-service analytics they still hunger for deeper insights and yet more self-service. Read this research for insights into creating a truly broad-based and self-service data-driven culture.

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Watch the webinar

Watch: Empowering the “analytical workforce”

See Aberdeen Group VP and Principal Analyst Michael Lock discussing findings from his latest research into analytics and serving a new generation of customers.Topics include the top strategies for understanding and meeting the needs of today’s customers, and how to empower the “analytical workforce”.

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This combination delivers faster answers, better decisions, and smarter actions. For nearly 20 years, thousands of businesses around the globe have relied on TIBCO technology to differentiate themselves through compelling customer experiences, optimized assets, and innovative new business models. For more information about TIBCO, visit tibco.com

TIBCO Software takes businesses to their digital destinations by interconnecting everything in real time and providing augmented intelligence for everyone, from business users to data scientists.

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