Get them to work for free

When Marco Hansell launched Speakr (originally called twtMob) in 2010, he had a

clear business concept: a venture that would coordinate influencer-based social

media campaigns via Twitter for Fortune 500 brands. He was also clear about the

kinds of programmers, developers, designers and copywriters he needed to help

launch it. 

“I knew I wouldn’t have the money to pay them, so I needed to figure out how to

make it beneficial to them without costing me a lot,” explains Hansell, who serves as

CEO of the Los Angeles-based company. “It came down to me selling them on a

story and a vision they could believe in.”

Hansell doled out IOUs to the six contractors who did eventually buy into his

vision. Each signed a convertible note agreement, whereby the moment the

company saw sales of $250,000 or attracted its first $1 million in venture

capital, they could either take their full salary for their work up to that point, in cash,

or opt for an equity share in the company commensurate to their back salary, plus

20 percent. 

“We structured it so there was a little risk on both sides,” Hansell says. “They knew

we weren’t just using them to make a quick buck, that we’d be making a shared

sacrifice. That’s how we got buy-in.”

The results: Within a year of launch, twtMob hit the $250,000 revenue trigger;

within 18 months it had generated close to $2 million in sales.

“Being resource-constrained made us very grounded, very resourceful and very

smart about our decisions,” Hansell says. “We did a smaller number of things at a

really high quality.”

That approach has paid off for all involved. Last fall, Speakr landed a second round

of VC funding, while the contractors who opted for an equity stake over cash now

“own a pretty sizable percentage of the company relative to the amount of work they

did” early on.

Building a data-driven strategy

Executives should focus on targeted efforts to source data, build models, and transform organizational culture.

Big data and analytics have climbed to the top of the corporate agenda. Together, they promise to transform the way companies do business, delivering the kind of performance gains last seen in the 1990s, when organizations redesigned their core processes. And as data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.In our work with dozens of companies in six data-rich industries, we have found that fully exploiting data and analytics requires three mutually supportive capabilities. First, companies must be able to identify, combine, and manage multiple sources of data. Second, they need the capability to build advanced-analytics models for predicting and optimizing outcomes. Third, and most critical, management must possess the muscle to transform the organization so that the data and models actually yield better decisions. Two important features underpin those competencies: a clear strategy for how to use data and analytics to compete and the deployment of the right technology architecture and capabilities.

Just as important, a clear vision of the desired business impact must shape the integrated approach to data sourcing, model building, and organizational transformation. That helps you avoid the common trap of starting by asking what the data can do for you. Leaders should invest sufficient time and energy in aligning managers across the organization in support of the mission.

1. Choose the right data

The universe of data and modeling has changed vastly over the past few years. The volume of information is growing rapidly, while opportunities to expand insights by combining data are accelerating. Bigger and better data give companies both more panoramic and more granular views of their business environment. The ability to see what was previously invisible improves operations, customer experiences, and strategy. That means upping your game in two areas.

Source data creatively

Often, companies already have the data they need to tackle business problems, but managers simply don’t know how they can use this information to make key decisions. Operations executives, for instance, might not grasp the potential value of the daily or hourly factory and customer-service data they

possess. Companies can encourage a more comprehensive look at data by being specific about the business problems and opportunities they need to address.

Managers also need to get creative about the potential of external and new sources of data. Social media generates terabytes of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to weather forecasts. One way to prompt broader thinking about potential data is to ask, “What decisions could we make if we had all the information we need?”

Get the necessary IT support

Legacy IT structures may hinder new types of data sourcing, storage, and analysis. Existing IT architectures may prevent the integration of siloed information, and managing unstructured data often remains beyond traditional IT capabilities. Fully resolving these issues often takes years. However, business leaders can address short-term big-data needs by

working with CIOs to prioritize requirements. This means quickly identifying and connecting the most important data for use in analytics and then mounting a cleanup operation to synchronize and merge overlapping data and to work around missing information.

2. Build models that predict and optimize business outcomes

Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model usually starts, not with the data, but with identifying a business opportunity and determining how the model can improve performance. We have found that such hypothesis-led modeling generates faster outcomes and roots models in practical data relationships that are more broadly understood by managers.

Remember, too, that any modeling exercise has inherent risk. Although advanced statistical methods indisputably make for better models, statistics experts sometimes design models that are too complex to be practical and may exhaust most organizations’ capabilities. Companies should repeatedly ask, “What’s the least complex model that would improve our performance?”

3. Transform your company’s capabilities

The lead concern senior executives express to us is that their managers don’t understand or trust big data–based models and, consequently, don’t use them.

Such problems often arise because of a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. The new approaches either don’t align with how companies actually arrive at decisions or fail to provide a clear blueprint for realizing business goals. Tools seem to be designed for experts in modeling rather than for people on

the front lines, and few managers find the models engaging enough to champion their use—a key failing if companies want the new methods to permeate the organization. Bottom line: using big data requires thoughtful organizational change, and three areas of action can get you there.

Develop business-relevant analytics that can be put to use

Many initial implementations of big data and analytics fail because they aren’t in sync with a company’s day-to-day processes and decision-making norms. Model designers need to understand the types of business judgments that managers make to align their actions with broader company goals. Conversations with frontline managers will ensure that analytics and tools complement existing decision processes, so companies can manage a range of trade-offs effectively.

Embed analytics in simple tools for the front lines

Managers need transparent methods for using the new models and algorithms on a daily basis. By necessity, terabytes of data and sophisticated modeling are required to sharpen marketing, risk management, and operations. The key is to separate the statistics experts and software developers from the managers who use the data-driven insights. The goal: to give frontline managers intuitive tools and interfaces that help them with their jobs.

Develop capabilities to exploit big data

Even with simple and usable models, most organizations will need to upgrade their analytical skills and literacy. To make analytics part of the fabric of daily operations, managers must view it as central to solving problems and identifying opportunities. Efforts will vary, depending on a company’s goals and desired time line. Adjusting cultures and mind-sets typically requires a multifaceted approach that includes training, role modeling by leaders, and incentives and metrics to reinforce behavior. Adult learners, for instance, often benefit from a “field and forum” approach, in which they participate in real-world, analytics-based workplace decisions that allow them to learn by doing.

Our experience suggests that executives should act now to implement big data and analytics. But rather than undertaking massive change, executives should concentrate on targeted efforts to source data, build models, and transform the organizational culture. Such efforts help maintain flexibility. That’s essential, since the information itself—along with the technology for managing and analyzing it—will continue to grow and change, yielding new opportunities. As more companies learn the core skills of using big data, building superior capabilities will become a decisive competitive asset.

The 4 keys to running a data-driven business

Image Credit: Emka74 / Shutterstock

December 4, 2013 11:40 AMJim Baer / LinkedIn

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This post is from LinkedIn data scientist Jim Baer, who’s speaking this afternoon at

VentureBeat’s DataBeat conference in Redwood City.

Today, businesses have access to data far beyond anything data-focused corporations had 20 years

ago. The massive amounts of valuable data garnered from things like staff behavior and customer

interactions can become a company’s biggest competitive advantage.

But how many of us are making the best use of that data? A data-driven approach to business means

using all that information to optimize existing business goals and investigate new possibilities. This

can mean conducting frequent experiments to make sure you’re selling as much of your current

offerings as possible. Such tests can be as simple as checking whether to put the mustard on the

shelf next to the ketchup or the hot dogs or as sophisticated as testing whether a feature personalized

for each visitor to a webpage can raise both engagement and revenue.

You can also use data to make recommendations, inferring what will bring the greatest value to

your customers based on their history and characteristics. At LinkedIn, we do this to connect our

members with opportunity, whether it is identifying job openings that most suit a member’s career

experience or by recommending groups of like-minded professionals to engage in expert discussions.

Becoming a data-driven business can seem daunting, especially with a crowded market of companies

selling products and services that promise to unlock the power of your data. In my experience, there

are four foundational supports necessary in crafting a data-driven approach to business, although

your level of investment in each can vary widely depending on your company’s goals and resources.

1.       Build the Right Data Infrastructure for the Company’s Goals

Data infrastructure is the underlying technological plumbing that collects, transmits, stores,

and delivers data to be leveraged for monitoring the business and understanding

opportunities.  Without a solid data infrastructure there will not be a reliable source of data to guide

decisions.

However, there is no one-size-fits-all solution to creating a data infrastructure; there will always be

trade-offs between the cost of collecting and wielding data and the benefit for business goals.

For example, a gaming company may want to collect all of the data on how users play its games in

order to create effective features and grow the business. This will require investing in a huge

relational database that allows those building the games to ask a broad variety of questions.

However, if a winery is simply trying to understand how many people are visiting their website and

what types of actions they take on the site, they can rely on inexpensive (or even free) services to get

those answers. They can then focus their resources on tracking the elements in their wines that best

resonate with customers to craft future best-sellers.

In any case, approach the data infrastructure investment decision with a specific set of goals in mind,

but retain flexibility in the system wherever possible. As your business grows and evolves, your needs

will change (likely increase) and may require adjustments to infrastructure.

2.       Democratize Data Throughout the Company

Data infrastructure investments won’t provide value unless the data collected is accessible.

The more people who can access and use data to measure performance, evaluate

improvements, and learn about the business and customers’ patterns, the better.

Democratized data allows employees outside of the technical departments to critically evaluate the

company’s data and ponder implications for the business. It allows the right person, with the best

context on a specific area of the business, to directly evaluate whether the data supports

expectations. It also empowers a broad base of employees to find anomalies that can be important

opportunities or warnings.

For example, members of one product team might want to explore the sales impact that another team

saw when they changed serving sizes for a beverage product. Or, for an online dating website, the

same sudden rise in site traffic that delights the monetization team may concern the security team,

which suspects a bot attack. The key is to get data into the hands of those who recognize what it

means and for that data to correspond to clearly-defined metrics.

3.       Enable Experimentation

Experimentation tools provide the ability to test innovations and treatments and learn from the

performance data before making big bets. The simplest approaches to experimentation are before-

and-after types of evaluations to understand the effects of making a singular change to a test object.

However, most business questions call for more complex experiments, such as which features an

auto insurance company should add to policy offerings to increase renewal rates among customers.

This may involve multiple test groups of policyholders and a slew of different features to test and

compare results.

The best experimentation systems will streamline the creation and tracking of test groups, treatments,

and results to help simplify the process and scale it across an organization. But even a well-designed

testing platform needs careful experiment design to maximize the opportunity for genuine learning.

4.       Foster a Data-Driven Culture

A data-driven business culture requires the infusion of data to optimize familiar processes; it also

requires a company-wide philosophy of innovation and experimentation, where employees are

constantly seeking opportunities for new breakthrough products or features.

You can foster a data-driven culture by always asking for and consulting the data when making

decisions. This works best when data is demanded by top-level employees, requiring hard numbers

to back up claims of the benefits that a new program or feature will bring. In a data-driven culture,

you’re always asking the question “What do the numbers show?”

For example, at LinkedIn, the heads of each product group give a weekly presentation to executives

in which they present the primary metrics for their business lines and discuss any notable changes

from plan.

The tools and infrastructure laid out above can be made or bought to fit your business goals with

various levels of expense and expertise. However, the investment in these will not yield the fruit of

data-driven success unless you also establish a company culture that requests evidence from data as

part of the standard decision-making process.

As we enter a new era where data is more easily accessed by companies of all sizes, those who

begin to leverage the massive amounts of unique data for their company will enjoy a competitive

advantage. Those who don’t will eventually be left behind.

Jim Baer is senior director of Data Science at LinkedIn.

The Economist: Fostering a data-driven culture

This report explores the challenges in nurturing data-driven decision making, and what companies can do to meet them. See preview.

The importance of data-driven thinking is not new. Many executives are familiar with the concept. The rise of data-

driven companies, from Facebook to Walmart, shows how powerful the approach can be. But what does it mean in

practice? And what are the benefits of adopting a data-driven culture within an organisation?

Let us start with what a data-driven culture is not. It is not a belief that data are an issue for someone else in the

company, a job for a data specialist or perhaps the IT department. There is still a perception that a data specialist,

perhaps a recent statistics graduate, should be parachuted in to an organisation to advise on how to work magic

with data, much as a computer security expert would be called on to help shore up a company’s IT networks.

This is flawed thinking. IT security is indeed a job for experts, but data are everyone’s business. Forward-looking

companies are integrating data into their day-to-day operations. They are placing data at the heart of almost all

important decisions. And they are tolerant of questioning—even dissent—about business decisions being made, as

long as the questioning is based on data and their analysis. This is what it means to adopt a datadriven culture.

An Economist Intelligence Unit survey of 530 senior executives, sponsored by Tableau Software, together with

interviews with four leading industry experts, delves into this trend and highlights best practices. Evidence from

these exercises shows that data are gaining a foothold within all parts of organisations, even in areas where they

have previously had little impact. The survey and interviews also highlight the tensions involved in democratising

data, and some of the methods that can be used to defuse them.

Perhaps most importantly, this report echoes a critical point that data advocates make repeatedly: working with data

is good for a company’s bottom line. There is abundant anecdotal evidence in favour of this claim—retailers like

Tesco have used data to gain market share and casinos have reaped rewards by turning marketing into a science.

Our survey backs this up with evidence that links financial performance and the successful exploitation of data. It is

a reminder that a focus on data can transform businesses.

Appreciating the financial power of data

The survey reveals a clear link between financial performance and use of data. Eleven percent of respondents

state that, in comparison to peers, their organisation makes “substantially” better use of data. But top-performing

companies comprise more than a third of this group, demonstrating the connection between datadriven decision-

making and organisational performance. And the reverse is true for underperforming companies. Seventeen percent

of executives identified their companies as lagging behind peers in financial performance. Among this group, not a

single one claimed that his or her organisation held a substantial advantage over rivals when it comes to use of

data.

The benefits of data are being seen in almost all parts of companies. When asked to rate the importance of data to

different organisational units, 43% of respondents say that data are “extremely important” to strategic decision

making. This figure is higher than that for any other unit, but there are many other areas where respondents say

data are yielding benefits. Just under 40% say data are extremely important to marketing and communications, as

well as finance and accounting.

Some areas remain relatively untouched by data, but probably not for long. Just 11% of respondents rate data as

extremely important to the human resources (HR) function, for example. A new crop of start-ups is trying to change

that. At one, TalentBin, engineers have built software that scours LinkedIn and social media, from Twitter to Quora,

to build a profile designed for recruiters. It is based on the idea that the best candidates for a position are not

necessarily looking for a new job, but might be open to being approached about one. TalentBin uses online data to

identify those people, and it seems to be working: since launching in May, the company has signed up clients like

eBay and Dolby.

The survey also reveals that data-driven companies have an expansive attitude to data use by employees. Almost a

third of respondents at companies that lead peers in data use say that employees across the organisation should be

applying data analysis techniques compared with 17% at companies that trail peers in data use.

Share data and prosper

Appreciating the power of data is, of course, only the first step on the road to a data-driven culture. For older

companies, especially those that have achieved success with minimal use of data, the transition to a data-driven

culture does not

necessarily come naturally. “Many of my clients are clearly aware of the importance of data,” says Jerry O’Dwyer, a

principal at Deloitte Consulting. “But they don’t know where to start in terms of where they should focus to get the

most value, as well as how to translate the data into actionable insight.” “Becoming data-driven is very difficult

for many executives,” agrees William Schmarzo, chief technology officer at EMC, an information technology

company. “They are reluctant to turn over decision-making to people who make decisions on the basis of data

rather than expertise.”

Our survey provides guidance for executives who want to make the change. Data often exist in silos, for example,

sometimes overseen by protective divisional heads. But more than half of respondents from top-performing

companies say that promotion of data-sharing has helped generate a data-driven culture in their organisation.

Moving data collection to the centre of a company is another example. Data collection is cited as “very

important/essential” to data culture by 76% of executives from top-performing companies compared with 42% from

companies that lag their peers.

Increased availability of training is a further factor to consider. Around one in three respondents say it is “very

important” to have programmes or partnerships in place to make employees more data-literate. Awareness of this

need is even higher among executives at companies that out-perform their peers financially; 50% of respondents

from this group rate training as highly important.

The survey also provides some suggestions for what not to do. Issues around sharing data appear to be the biggest

challenge. About one-third of respondents say that their company struggles to achieve a data-driven culture in part

because of concerns about the privacy and security issues that arise when data are shared. Just over 30% of

respondents attribute a reluctance by department heads to share data as a cause for failing to realise a data-driven

culture.

"BIG DATA" - DATA DRIVEN BUSINESSThere are many definitions of “Big Data” going around. We are convinced that the most important application of Big Data is the use of data in real time to make predictions and take actions assuring growth and margins. Before the term Big Data was created it still existed as a method, using as much data as possible to make smart decisions.

Big Data is how you can grow margins and market share with the help of all “your” data. And “your” data is not only what you have in your own databases, it is also what you have

available on the internet. The challenge is how to find it, how to gather it, how to analyze it and how to act on it. And should it be used for:

Acquisitions Cross selling Retention Cost reduction Resource optimization All of the above?

Big Data leads to competitive advantage

Big Data is “The next frontier for innovation, competition, and productivity”. And there are a growing number of both B2C companies and of B2B companies who use their data in a way that increase performance. However, collecting and analyzing the data is not enough — it must be presented in real time to influence decisions and make sure actions are taken as a direct consequence. It can have a material impact on the productivity, profitability or efficiency of the organization but Gartner predicts through 2015, more than 85% of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage.

“Is this really something for my company?”

According to a 2013 publication by McKinsey & Company, the payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt. Successful case studies and research show that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition.

However, most organizations are ill prepared to address both the technical and management challenges posed by big data; as a direct result, few will be able to effectively exploit this trend for competitive advantage. At least in the near future.

According to Gartner predictions approximately two-thirds of all companies are investing, or are planning to invest, in Big Data. Are you one of those – or not?

What happens to those who do not start in time?

From historical data to real time

It started as a computer driven business in the 50s and has not changed a lot. BI is still a huge business even if it uses historical data and older technology. But with the new, vast amounts of data that is created and the tough market place with global competition for all industries, real time decision making is crucial to gain market share. Batched technology and inhouse data does not match the challenge. The right product to the right price to the right customer at the right time is the way forward. The old shotgun approach is dead.

When the data is unstructured everyone must be a big data analyst

Findwise’s interest and expertise is not limited to structured data, but instead to join structured and unstructured data as well as create structured data – fact-finding – by mining a big repository of unstructured data.

The modern world produces massive amounts of data that, to a large extent, is unstructured and transient. It comes from a variety of sources and types – as text, video, geospatial data, information captured by a sensor in a plant or a vehicle, or from social interaction via the web. Data volumes double every two years meaning the sheer amounts of data is staggering compared to only a few years back. With the knowledge that 75% of all new data is created by the consumer it is imperative to find and analyze that data. It is now possible due to new technology developments to gather, store and analyze large data sets at a much lower cost than before. Familiarity with data analysis becomes part of the skill set required of ordinary business users, not experts with “analyst” in their titles.

BI looks at what happened – we predict in real time what is going to happen

There are many examples of enterprises outperforming their peers when using data driven decision tools. But when traditional BI is a tool for becoming an expert of past events to try to look forward, we are experts of predicting the future in real time. We use Big Data for real time predictive analytics.

For the last few years Findwise has seen the opportunity to apply our own Language Technologies and Content/Data Processing framework within this field, in research as well as in practice. A few examples are Social Media Monitoring to identify influencers and apply

sentiment analysis of various products and services, Predictive Maintenance in the process industry, Pattern Mining in Electronic Health Records to identify relationships between diagnosis, medicine and comorbidity in Health-Care, Dialogue Systems within cars , and many other areas.

Creating operational visibility is another area of applied Big Data where we have experience from several industries:

The gaming industry where our dashboards with drill downs from visualized traffic lights on the highest level provides Operational Intelligence from more than 80 platforms globally.

Logistics - Creating full operational visibility in logistics process and creating full operational visibility on customer facing API:s

Banking - call center aiming for enhanced response time and proactivity through visibility on what the client did in real-time when the call is done.

eCommerce - Operational visibility on system landscape at one of the largest eCommerce site in the Nordics.

We help you get started with data driven business

Findwise is offering a complete set of services in order to help our clients take the step into the data driven business world. Strategy workshops - management training – business analysis - building the right business system that is taking care of all the new possibilities that comes with data driven business.

It is not about the data – it is how you act on it…

An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math

Walter FrickMAY 19, 2014

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Not a week goes by without us publishing something here at HBR about the value of data in business. Big data, small data, internal, external, experimental, observational — everywhere we look, information is being captured, quantified, and used to make business decisions.

Not everyone needs to become a quant. But it is worth brushing up on the basics of quantitative analysis, so as to understand and improve the use of data in your business. We’ve created a reading list of the best HBR articles on the subject to get you started.

Why data matters

Companies are vacuuming up data to make better decisions about everything from product development and advertising to hiring. In their 2012 feature on big data, Andrew McAfee and Erik Brynjolfsson describe the opportunity and report that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors” even after accounting for several confounding factors.

This shouldn’t come as a surprise, argues McAfee in a pair of recent posts. Data and algorithms have a tendency to outperform human intuition in a wide variety of circumstances.

Picking the right metrics

“There is a difference between numbers and numbers that matter,” write Jeff Bladt and Bob Filbin in a post from last year. One of the most important steps in beginning to make decisions with data is to pick the right metrics. Good metrics “are consistent, cheap, and quick to collect.” But most importantly, they must capture something your business cares about.

The difference between analytics and experiments

Data can come from all manner of sources, including customer surveys, business intelligence software, and third party research. One of the most important distinctions to make is between analytics and experiments. The former provides data on what is happening in a business, the latter actively tests out different approaches with different consumer or employee segments and measures the difference in response. For more on

what analytics can be used for, read Thomas Davenport’s 2013 HBR articleAnalytics 3.0. For more on running successful experiments, try these two articles.

Ask the right questions of data

Though statistical analysis will be left to quantitative analysts, managers have a critical role to play in the beginning and end of the process, framing the question and analyzing the results. In the 2013 article Keep Up with Your Quants, Thomas Davenport lists six questions that managers should ask to push back on their analysts’ conclusions:

1. What was the source of your data?

2. How well do the sample data represent the population?

3. Does your data distribution include outliers? How did they affect the results?

4. What assumptions are behind your analysis? Might certain conditions render your assumptions and your model invalid?

5. Why did you decide on that particular analytical approach? What alternatives did you consider?

6. How likely is it that the independent variables are actually causing the changes in the dependent variable? Might other analyses establish causality more clearly?

The article offers a primer on how to frame data questions as well. For a shorter walk-through on how to think like a data scientist, try this post on applying very basic statistical reasoning to the everyday example of meetings.

Correlation vs. cause-and-effect

The phrase “correlation is not causation” is commonplace, but figuring out just what it implies in the business context isn’t so easy. When is it reasonable to act on the basis of a correlation discovered in a company’s data?

In this post, Thomas Redman examines causal reasoning in the context of his own diet, to give a sense of how cause-and-effect works. And BCG’s David Ritter offers a framework for deciding when correlation is enough to act on here:

The more frequent the correlation, and the lower the risk of being wrong, the more it makes sense to act based on that correlation.

Know the basics of data visualization

Rule #1: No more crap circles. To decide how to best display your data, ask these five questions. Make sure to browse some of the best infographics of all time. And before you present your data to the board, consult this series on persuading with data. (Don’t forget to tell a good story.)

Learn statistics

A couple of years ago, Davenport declared in HBR that data scientists have the sexiest job of the 21st century. His advice to the rest of us? If you don’t have a passing understanding of introductory statistics, it might be worth a refresher.

That doesn’t have to mean going back to school, as Nate Silver advises in an interview with HBR. “The best training is almost always going to be hands on training,” he says. “Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”

Walter Frick is an associate editor at the Harvard Business Review. Follow him on Twitter @wfrick.