Our vision has always been to enable business people to make smarter decisions so they can turn their data into a source of revenue, profit and competitive advantage. Our mission is to make that insight effortless and real-time for users and their companies, by running and managing GoodData’s powerful infrastructure ourselves.

Our cloud-based platform and apps — mash-ups of reports, analytics, metrics and best practices —  are the result. They prove that big data can be user-friendly. Our apps simultaneously crunch data from the Web, mobile devices, social media and legacy systems, and generate a visually stunning  user interface can easily understand. The result? Better data, better insights and greater  monetization. This year’s CODiE award, announced by SIIA at All About the Cloud conference, offers gratifying proof that we are exceeding our mission.

We’re excited and humbled to receive our CODiE because it means that our years of work and dedication have truly paid off for our users — who have always been our  number one priority.

Once again, we  thank you all.

Contact us to learn more about GoodData.

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I couldn’t have made that statement five years ago. What’s changed is a new generation of powerful, best-of-breed cloud products that delivers vertical capabilities — like data analysis, collaboration or customer support — all working together through open APIs. It’s like the cloud version of Lego blocks that can be combined into fascinating new shapes. The result? Accidental innovation, as SaaS partners and customers mix and match apps and functions at will.

Today’s announcement between GoodData and Box shows what I mean. Because of our open APIs, the new GoodBox Bash provides a common data platform that makes it easy for customers to analyze how effectively teams collaborate on Box. Companies can understand, for example, how team members use shared content, see successful habits, and easily measure the value and effectiveness of content. Customers can start using the new GoodBox Bash practically “out of the box.”

Compare that experience with the smoke-and-mirrors press releases of partnerships we’ve come to expect from legacy software vendors — lots of noise signifying nothing.

And that’s my underlying point. When we and other SaaS companies announce joint products or partnerships, it actually means something. This is the beauty of the new “cloud economy.” Imagine being able to combine and immediately deploy cloud-based apps from amazing SaaS companies — like Box, Zendesk for customer service, Zuora for online billing and GoodData for data analysis. I like to think of it as best-of-breed finally putting inbred out of its misery. (Or at least, ending of the misery of enterprises chained to the likes of Oracle, SAP and IBM.)

Just think of the ramifications for IT organizations! After more than 20 years of promise, the tech industry has finally liberated IT to focus on business priorities. That means quickly giving users powerful software that does exactly what they need, with the scale and reach of the cloud. Accent on “quickly” and “powerful” — two attributes that define the new generation of best-of-breed cloud apps and platforms.

I’ll use GoodData as an example. Our GoodData platform handles a mind-numbing array of big-data software built to handle tasks like data processing, data mining, programming, workflow and queries. And while these programs are incredibly powerful, they’re also amazingly complex. At GoodData, we handle all these programs at the back end so that our customers don’t have to. And that means our customers can quickly deploy powerful analytics across all their important data.

We aren’t alone in that philosophy. In fact, I’d say all great best-of-breed SaaS companies share the same mission and goal — which is to make it easy for business to consume technology.

Now, instead of worrying about flipping switches and keeping the lights on, IT folks take on new responsibilities related to the cloud — from governance, integration, capacity planning and security to vetting apps across all the lines of business. And that means the cloud economy satisfies another promise technology vendors have been making for years: Turning IT into a strategic partner that enables business value.

As I said: the cloud wins. Welcome to the new cloud economy.

Read Box’s blog or visit our website for more information on GoodBox.

The post The Cloud Economy: Putting an End to Inbred Software appeared first on GoodData.

A Sea Change in Trading

Stock trading is about being the first person to get an order on the books. But you don’t just want any order, you want to create a set of moves that maximize your potential while reducing risk.

For a long time, the first trader to jump on the phone and execute the right orders would win. Fifteen years ago, the Securities and Exchange Commission (SEC) allowed computerized stock trading for the first time. A new era of high-frequency trading (HFT) was born.

When traders realized the potential of computerized trading, they built algorithms that could analyze trades in record time and conduct high-volume trades in nanoseconds. Faster than humans, algorithms can adjust their strategies in real-time, testing the market by placing orders en masse, then canceling them if the market doesn’t provide the desired response.

The race to make the first and best trades quickly grew so tight that traders were placing their computers inside the data centers of some markets, reducing the distance that information packets had to travel to nearly nothing. As a result, the most wired traders were executing the best deals before everyone else, from banks to insurance companies to individuals. HFT became the only way to gain a real profit from short-term trades.

The Big Data Revolution

A similar arms race is occurring with big data today. If you ignore it, it’s like trying to conduct a trade on a rotary phone while others have their computers in the data center. Big data is speeding up and adding intelligence to the ways in which we define our progress, our markets and our potential.

Guesswork, experimentation and the hard labor of crunching numbers to make decisions have long been a part of business. Big data is, if not eliminating them, shrinking them significantly. Ignore it, and you’ll find yourself with outdated technology and an empty wallet. The moral of the big data story is turning into the same one as with computerized trading: Adapt or die.

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Buffett spends countless hours researching each of his equities. Before making a decision, he reads annual reports, news publications and any other information he can get his hands on. His decisions are informed by research, and based on the strict fundamentals that work best for his style of investing.

The way Buffett operates is not unlike the best use cases for big data. Among other things, his success lies in his ability to make good decisions in accordance with quality benchmarks. This is the key to using big data well, and to business success in general.

Big data users can learn from Warren Buffett. His brain, in effect, is a kind of big data engine. Here are three lessons that Buffett provides for big data users:

1. Know What You’re Looking For

By analyzing large amounts of data from diverse sources, we can gain context and correlations that wouldn’t have otherwise occurred to us. Buffett pulls from actuary reports and bond publications; big data can suck information out of nearly everything, from social media to expense reports.

But it is all for nothing if you don’t know what you’re looking for. You need structure around your queries.

Buffett has a few rules of thumb. He looks for low-volatility stocks with a low price-to-book ratio. They should be profitable and growing, among other considerations.

Similarly, if you ask your data analytics platform the right questions, you’ll know the best answers when you see them. Perhaps you’re looking for a Scotch-drinking customer base that prefers premium product and prioritizes convenience over shopping around. If you know that before you ask your data engine to generate their social media preferences, you’ll get a much better result than if you asked the same question about Scotch drinkers in general.

2. Understand How the Object of Your Research Works

Buffett once said: “Never invest in a business you can’t understand.” Given Buffett’s rational investment style, one can surmise the meaning behind that quote. If you don’t understand the mechanics of your investment, you’re relying too much on emotion and guesswork. Or else the business itself isn’t viable.

Likewise, if you don’t understand how the organization or customer base you’re running a big data query on works, you’re relying too heavily on assumptions. You could ask questions of the data that have nothing to do with the reality of the situation. Asking questions about the rate of email responses in a customer support organization that relies mostly on forums won’t get you very far.

3. Use Solid Benchmarks

Some of Warren Buffett’s more famous quotes illustrate the precision and simplicity of his benchmarks:

• “Rule No. 1: never lose money. Rule No. 2: don’t forget rule No. 1.”

• “Only buy something that you’d be perfectly happy to hold if the market shut down for 10 years.”

• “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”

These benchmarks demand the best. They don’t compromise. Arrange your own benchmarks to similar standards, ask the right questions, and make data driven decisions that reflect your high standards and style.

Make Data Your Servant

Maybe one of Warren Buffett’s biggest secrets to success is that he made the data work for him. If you aspire to do the same, data will quickly become your best friend.

Want to find the secrets hidden in your data? 

The post Warren Buffett, the Human Big Data Engine appeared first on GoodData.

But retail isn’t a one-size-fits-all proposition, even if you bring in a CEO with gold stars on his report card. By slashing prices and bringing in hipster apparel, the new CEO lost the retailer almost $1 billion. Despite his former success, he failed to address Penney’s core market of budget shoppers who prefer form-forgiving clothing.

The Wisdom of Data

Think of how the situation would have been different if, rather than relying on intuition and dismissing data to the contrary, the Penney team looked at data to guide their plans. Knowing that their core audience prefers conservative clothing, Penney could have found appropriate new brands for shoppers. By reading the numbers of which types of pricing work best, Penney could have been more strategic about its discounting approach.

It should be a lesson for us all. Company leaders—CEOs and boards—should hold decision-makers accountable for making data driven decisions. Otherwise, the risk of a gut-based disaster like Penney’s becomes very real.

C-Suite Data Insights

It is not enough to simply say that decisions should be data driven. CEOs should hold each of her direct reports accountable for providing visibility into their piece of the data pie. No one should be making decisions based solely on intuition or gut. The CEO has to demand visibility into all aspects of the business, so she can make the best strategic decisions for the entire company.

The VP of Sales should provide visibility into the sales funnel for the quarter. In addition, he should be showing how this quarter is performing against the historical ramp, so problem with funnel can be identified early and action can be taken.

It is the CMO’s job to use data analysis to innovate new solutions for branding, company reputation, customer targeting and product pricing. Car insurance companies, for example, are delivering different rates to people who drive to the same destination, but use different routes to get there, according to Forbes.

The COO should be optimizing the company’s resources, from equipment to carbon-neutrality. Analytics make it possible to ensure that every moving part of the business is performing at benchmark levels and is in line with company budgets.

Using analytics, the CFO should discover new financial efficiencies across departments. He can trim the fat in an organization, reduce financial risk and use data to drive budget decisions.

Analytics provide a high level of detail about customers, and the systems driving this visibility are powerful and sometimes complex on the back-end. The CIO must ensure the health of all the IT systems behind the company’s big data analytics. If they’re in-house, the CIO should have a solid maintenance and tuning regimen; if analytics are SaaS-based, the CIO is in charge of setting them up and collaborating with service providers to ensure seamless execution.

The CEO has the Final Word

By analyzing massive quantities of data from diverse sources, analytics can be used to pinpoint trends and new opportunities that otherwise would never be discovered. It is up to the CEO to gather information from each team member and decide how data results should best be used to increase the company’s competitive edge. Armed with data insights, she can use the collective experience to make the best decision. But for this to happen, the CEO is on the hook to make data and data visibility an integral part of the company’s culture.

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The following guest blog was written by John O’Farrell, partner at Andreessen Horowitz, and GoodData investor and board member.

On my Andreessen Horowitz blog, I told the story of how a massive software licensing deal with EDS in 2002 saved our money-losing cloud services company, Loudcloud, and enabled us to start life again as a data center software provider, renamed Opsware. EDS agreed to pay us a total of $52 million in quarterly installments to license our software over three years, ramping up to $20 million a year by Year Three.

What’s less well-known is that, 18 months later (halfway through the contract term), EDS still had not deployed a production instance of our software on any of the tens of thousands of servers for which they were paying us a quarterly subscription fee.

There were many good reasons for this: 1) Our software was not initially suitable for use by anyone other than us. 2) Their big-company inertia. 3) Our focus on signing additional customers. Even so, the inescapable fact was that we had a year-and-a-half to get EDS to use our software in a big way, or we’d lose a $20 million annual revenue stream. As a public company, the disclosure alone that EDS declined to renew would have killed us.

So here is what we did about it:

1. We assigned a dedicated account management team. Comprising top managers and engineers, the team focused on getting our software fully deployed — and used — at EDS.

2. We developed and executed a full plan. We cast a critical eye on everything we needed to do to keep EDS happy — from product changes and deployment tools to training, documentation, connecting with executives and evangelization.

The result is that EDS not only deployed our software, but it also renewed the license in 2005 for another $50 million.

Farmers vs. Hunters.

Back in 2002, a software subscription deal like ours with EDS was highly unusual. All enterprise companies sold their software as one-time perpetual licenses. With no recurring revenue apart from maintenance fees, you had to start from scratch every quarter to keep feeding the revenue engine — but you got paid regardless of whether the customer ever used your software.

By contrast, today’s successful SaaS businesses like GoodData start each quarter with a big chunk of monthly recurring revenue from customers signed in prior quarters. That makes SaaS businesses very attractive to investors once they get that initial recurring revenue flywheel spinning. It also means that getting existing customers fully deployed, as well as keeping them highly satisfied, is absolutely critical. In a SaaS business, churn kills.

While that may sound obvious, losing customers at contract renewal time is the Achilles Heel of many a SaaS startup, for a couple of reasons:

1. Chronology:  The company’s focus in its first year has to be on winning customers.  Renewals, by definition, come later.

2. Incentives:  The company’s board and management often emphasize winning new customers over making existing ones happy — and reward salespeople accordingly.

Some companies choose to address this by compensating their salespeople for renewals and upsells — not just new bookings. While there is some merit to doing this, I don’t see it as a full solution. Getting a customer fully engaged and deriving full value from your product is an ongoing project, not a one-time event like landing a new booking. It demands a different mindset — more of a farmer mentality than a hunter. Most salespeople are hunters. That’s good, since you want your sales team focused on winning new customers to keep the business growing. But it’s not enough.

A better answer is to create a dedicated Account Management function — like we did for EDS — with the accountability for keeping customers happy and renewed, and with the authority to ensure customers’ needs are met.

This goes way beyond technical support. Instead of focusing on the technical weeds, the team concentrates on helping customers achieve business success. That means understanding customers’ use cases, and working with them to ensure the products or services deliver the business value they crave. The best teams also act as ombudsmen — providing access to anyone inside the company who can offer the expertise or insight their customers require.

That mindset practically guarantees 100 percent renewals. But there are additional benefits, as well. These include:

• Giving customers a single point of contact.

• Creating an internal champion for customers by connecting them with the experts they need.

• Uncovering upsell opportunities (expanding use within the current environment) and cross-sell opportunities (finding new opportunities in other customer projects and departments).

• Building a rich set of customer success stories and references.

• Helping manage customer expectations.

• Providing an ongoing feedback loop for the product.

What It All Means

Investing in a dedicated account management function is an investment in customers’ success. GoodData recently made that investment, putting in place a fully staffed account management team under Jonathan Cogan. If you’re a GoodData customer, you can expect regular, consistent business reviews with his team so that they understand your goals and what they need to do to help you get there.

And if you’re an up and coming SaaS company, you need to think about how you can keep those customers you’ve spent so much on to acquire. It’s time to recruit some farmers!

Image Courtesy of Colorado State University

The post Farmers Versus Hunters: The Importance of Account Management appeared first on GoodData.

As our awareness of the environmental problem increases, so do the capabilities of our technology. We could very well innovate our way out of the most dire environmental problems.

Big data is playing a promising role. Here are some of the areas where big data is making a difference:

Measuring the Ocean
Australia’s Integrated Marine Observing System (IMOS) collects information from a huge network of underwater sensors. Video cameras and measuring devices are mounted on vehicles, ocean observation stations, satellites and even on ocean animals. The sensors read any number of indicators, from species movements to ocean temperature, salinity and carbon storage. More than 300 researchers receive data from IMOS, resulting in the publication of 1,000 or more scientific studies to date, according to the Human Face of Big Data.

Mapping Forest Damage and Animal Habitat
The Carnegie Airborne Observatory monitors large-scale changes to land environments via spectroscopes and lasers mounted on a twin prop plane. This flying big-data gathering device has, so far, mapped the chemical composition of the Amazon rainforest to track carbon impact, discovered why different plant species filter chemicals and carbon the way they do, and more.\

Instant WILD, an iPhone app, sends images captured by cameras placed in remote, wild locations to people who have downloaded the app on their mobile devices. When people see animals on the app, they send a notification to the Zoological Society of London, which owns the app. This saves scientists hours of staring at video footage—and hastens the discovery of information about important animal species.

Bio-Engineering Fossil Fuel Replacements
An array of companies are developing potentially game-changing technologies like carbon capture and biofuels. Synthetic Genomics is using big data genome sequencing to custom-design organisms, such as bacteria and algae, to perform tasks such as energy production. These organisms could eventually replace fossil fuels.

Corporate Sustainability
Many companies already know how many gallons of water they consume a month, and how many tons of waste they produce. With big data analytics, businesses can take that knowledge a step further and use it to inform the timing and nature of more environmentally friendly decisions. A company could note, for example, that summers are growing hotter, leading to a greater air-conditioning expense for the foreseeable future. The company could make the decision to invest in becoming more energy-efficient sooner rather than later.

The Bottom Line
Measurement and analysis lead to new insights. Those insights lead to better decisions. That is the bottom line with big data. When it comes to the environment, we have a track record of poor decisions. It’s time to reverse that trend, and big data is poised to be a key player for years to come.

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Executives ignore this development at their own peril. Data skills are a key component to growing a business, keeping an organization healthy and even, increasingly, climbing the career ladder. In the not-too-distant future, those without analytics abilities will be left behind.

The good news is that regardless of specialization, there are a handful of general ways that any executive can apply data analytics to improve their results and job performance. Here are the five most common ones:

1. Justify the Need for Resources and Prove ROI

Need more staff or budget? Data analytics is one of the best ways to prove your need to other stakeholders. By using analytics to monitor and evaluate your part of the organization, you can draw conclusions about which activities are driving the most results, inside and outside of your department.

Historical data can show you what activities bring the most ROI, and therefore should be supported most. If you’re in sales, for example, you might discover that most of your closed sales started with happy hour meetings, so you could grow your happy hour budget. You can also compare historical and current data to find weak spots—and make the case that you need to build your bench to fill them in.

2. Plan for the Future

Scenario analysis built on models, forecasts and other types of data can help you understand the most likely outcomes of decisions. Predictive analytics can help you identify the most likely scenarios for hiring, capacity planning, budgets, equipment needs and other considerations. Instead of relying on guesswork and near-term intuition, actively plan for the future.

3. Recruit the Right People

Hiring managers have long understood that the best resume doesn’t necessarily make for the best worker. Data analytics are already helping fill the gap between a fancy resume and the right characteristics. Large national retailers, for example, have a huge hiring burden, according to The Economist. A retailer might have 1 million employees and an annual attrition rate of 50%, meaning the company must hire 500,000 people per year. They’re using data analytics to look beyond resumes and applications to pinpoint the important habits that make the best employees. The Economist named a couple of unexpected correlations:

• People who deliberately install a browser, such as Chrome, rather than using their machine’s default browser are assumed to make better employees, perhaps because they’re proactive.

• For certain jobs, such as call center support, people with criminal records tend to perform better than those without.

Data analytics, in other words, adds new richness to the hiring of new workers.

4. Compensate and Reward Your Employees

Data can also simplify the process of identifying the best performers. Data brings a new level of granularity to the process. You might already know who your best closers are, but data might point to the person most responsible for filling the pipeline. If one of your helpdesk staff has the ability to answer her tickets the first time, she has a bigger impact on the business, because she’s not only more efficient than other workers, but is sending a positive brand association through every thorough answer. With data analytics, pinpointing such performers becomes easy, and it’s also possible to calculate financial rewards based on the real impact they have on the business.

5. Make the Right Decisions at the Right Time

Data analytics bolsters the probability of delivering the right service or information at the right time. Is it the right time to give your star performer a raise? Do you want to include an add-on service for that prospect you’re pitching, and should you contact them on Tuesday or Wednesday? What specific information should you include in your campaign geared at retired tech CEOs living in San Luis Obispo? Data analytics are important not just for giving you the right information, but for enabling you to execute at the best possible time.

All in all, big data analytics are your friend. Use them wisely and use them well. Also know that not all data analytics platforms are created equal—if it’s too complicated to understand, consider finding a platform that you can work with.

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Where Did Dashboard Reporting Come From?

Historically, business intelligence (BI) tools were designed for data specialists who would build customized reports using a complicated UI. These specialists generally had PhDs in data science, and they needed that kind of training to discern the information that their systems were spitting back out at them. At the time, BI had no dashboards, so data came in the form of a spreadsheet, and the data scientist had to draw correlations and conclusions from columns of numbers. Then, the specialists would build and compile individual reports.

Thanks to cloud and advances in technology, the concept of dashboard emerged. People realized that it’s important to have all pertinent business data in one place. That way, you can see the context of your data and track multiple KPIs next to each other. Finally, there was a visual way to consume and interpret data and make better decisions. Using dashboards, even those of us who aren’t data scientists could understand our businesses better.

How Dashboards Evolved

In the early days of dashboards, however, you still had to build your BI environment by yourself. Reporting tools like key performance indicators (KPIs) had to be constructed by the data scientist before they were put in the dashboard. So dashboarding was either a module or an add-on.

Today, dashboarding has become much easier. Although some BI solutions are still siloed, the newest wave of solutions lets you build, edit and move dashboards around with the same ease that you can move around, say, PowerPoint or Keynote slides. Still, not all dashboards are created equal.

What to Look For in Dashboard Reporting

In order to make sure you choose the right dashboard, you should look for three things:

1. Context on one screen

In traditional BI solutions, you have to navigate to another screen to build your reports before placing them on a dashboard. These separate environments create a psychological divide that makes it more difficult for users to orient themselves in the dashboard and understand the data models they’re building. If you create your report in a separate page, it’s hard to imagine what a chart will look like when it’s finished and put on a dashboard next to other charts, tables or images.

The trick is to find a solution that lets you have all of your context in one place. You’re never forced to go anywhere else. Everything is integrated and in one location. There is no divide between the dashboard and data analysis. Because you know where everything is at all times, your data doesn’t live in a vacuum. You can even find new insights by seeing charts side-by-side, and moving them around.

2. The ability to move things around

Building and moving KPIs around on your dashboard should be as easy as it is to do things on a mobile device—swipe, zoom, shrink, click on a dialog box to see something in detail. That way, you can still have your context all around your actions, so you don’t get lost in your data or confused by what you’re seeing. You can position everything perfectly, adjust the layers of your dashboard and edit it, without losing your context or the clean look and feel of your dashboard’s design.

3. Get information when, where and how you need it

In today’s real-time business environment, daily or weekly updates don’t cut the butter anymore. You should be able to get updates on demand, when and where you need them. Your mobile device should also be able to push you notifications — informing you that you just received a much-anticipated email, for example—to further simplify your decision-making. Your BI should be waiting on you, not the opposite.

To learn more about dashboards, visit our What is a Dashboard blog.
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Baseball is Still the Big Data Winner

Baseball season has long been big data season. The movie Moneyball, in which Oakland A’s coach Billy Beane used a new statistics-based approach to assemble a winning team, is one famous depiction of the process.

In real life, big data permeates almost every facet of baseball. According to The Human Face of Big Data, a network called MLB Advanced Media collects and manages data from every baseball game throughout the season, as well as during pre- and post-season. Video data is collected on everything that happens in the game, generating ongoing statistics on batting averages, strikes, fouls, home runs and other parts of the game.

PITCHf/x, a complementary system that takes about 60 photos per second to measure the speed and trajectory of each ball, is added to the MLB Advanced Media data stream. The stream is packaged and sent to broadcasters, stadium operators, network TV stations and others. A normal game night can generate 20 terabytes of media and statistical content, according to book. PITCHf/x alone sends 30 million images per season.

Sensors in Soccer, Water Displacement—It’s All Being Tracked

Baseball might have the most advanced big data system of our national sports organizations, but others are catching up. Last year in the NFL, the Baltimore Ravens announced the hiring of a new director of football analytics. The NBA uses cameras mounted on stadium rafters to follow players’ movements and track data, including the number of shots each player made, the Wall Street Journal reports. The amount of water being displaced by swimmers, the height being gained by long jumpers, tennis ball placement detection—all are gaining traction among the pros, according to the WSJ. European soccer players are on the field with sensors embedded in their clothing, which provide an ongoing feed of players’ field position and physical data.

Beyond Performance

Player performance isn’t the only place that big data is making more of an appearance. During the 2012 Olympic Games in London, the city ran a real-time situational awareness system that ran data from sensors on door locks, point of sales systems and other sources of activity inside and outside of the stadium, InformationWeek reported. By tracking this data, security officials could see unusual activity in real time. Then, of course, there’s advertising—big data was key to information management during the Super Bowl earlier this year.

May the Best Data Win

In an athletic world where tiny deviations mean the difference between winning and losing, big data is here to stay as a key way to gain the edge in sports. As measurement systems grow more advanced, we may find new and unexpected winning formulas for teams and individual competitors.

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