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How Corporate Culture Impedes Data Innovation

As seen in InformationWeek

Floppy disk

Corporate culture moves slower than tech

Competing in today’s data-intensive business environment requires unprecedented organizational agility and the ability to drive value from data. Although businesses have allocated significant resources to collecting and storing data, their abilities to analyze it, act upon it, and use it to unlock new opportunities are often stifled by cultural impediments.

While the need to update technology may be obvious, it may be less obvious that corporate cultures must also adapt to changing times. The necessary adjustments to business values, business practices, and leadership strategies can be uncomfortable and difficult to manage, especially when they conflict with the way the company operated in the past.

If your organization isn’t realizing the kind of value from its big data and analytics investments that it should be, the problem may have little to do with technology. Even with the most effective technologies in place, it’s possible to limit the value they provide by clinging to old habits.

Here are five ways that cultural issues can negatively affect data innovation:

1. The Vision And Culture Are At Odds

Data-driven aspirations and “business as usual” may well be at odds. What served a company well up to a certain point may not serve the company well going forward.

“You need to serve the customer as quickly as possible, and that may conflict with the way you measured labor efficiencies or productivity in the past,” explained Ken Gilbert, director of business analytics at the University of Tennessee Office of Research and Economic Development, in an interview with InformationWeek.

[ What matters more: Technology or people? Read Technology Is A Human Endeavor. ]

Companies able to realize the most benefit from their data are aligning their visions, corporate mindsets, performance measurement, and incentives to effect widespread cultural change. They are also more transparent than similar organizations, meaning that a wide range of personnel has visibility into the same data, and data is commonly shared among departments, or even across the entire enterprise.

“Transparency doesn’t come naturally,” Gilbert said. “Companies don’t tend to share information as much as they should.”

Encouraging exploration is also key. Companies that give data access to more executives, managers, and employees than they did in the past have to also remove limits that may be driven by old habits. For example, some businesses discourage employees from exploring the data and sharing their original observations.

2. Managers Need Analytics Training

Companies that are training their employees in ways to use analytical tools may not be reaching managers and executives who choose not to participate because they are busy or consider themselves exempt. In the most highly competitive companies, executives, managers, and employees are expected to be — or become — data savvy.

Getting the most from BI and big data analytics means understanding what the technology can do, and how it can be used to best achieve the desired business outcomes. There are many executive programs that teach business leaders how to compete with business analytics and big data, including the Harvard Business School Executive Education program.

3. Expectations Are Inconsistent

This problem is not always obvious. While it’s clear the value of BI and big data analytics is compromised when the systems are underutilized, less obvious are inconsistent expectations about how people within the organization should use data.

“Some businesses say they’re data-driven, but they’re not actually acting on that. People respond to what they see rather than what they hear,” said Gilbert. “The big picture should be made clear to everybody — including how you intend to grow the business and how analytics fits into the overall strategy.”

4. Fiefdoms Restrict Data Sharing

BI and analytics have moved out from the C-suite, marketing, and manufacturing to encompass more departments, but not all organizations are taking advantage of the intelligence that can be derived from cross-functional data sharing. An Economist Intelligence Unit survey of 530 executives around the world revealed that information-sharing issues represented the biggest obstacle to becoming a data-driven organization.

“Some organizations supply data on a need-to-know basis. There’s a belief that somebody in another area doesn’t need to know how my area is performing when they really do,” Gilbert said. “If you want to use data as the engine of business growth, you have to integrate data from internal and external sources across lines, across corporate boundaries.”

5. Little-Picture Implementations

Data is commonly used to improve the efficiency or control the costs of a particular business function. However, individual departmental goals may not align with the strategic goal of the organization, which is typically to increase revenue, Gilbert said.

“If the company can understand what the customer values, and build operational systems to better deliver, that is the company that’s going to win. If the company is being managed in pieces, you may save a dime in one department that costs the company a dollar in revenue.”

Six Ways to Master the Data-Driven Enterprise

As seen in InformationWeek.

StatisticsBig data is changing the way companies and industries operate. Although virtually all businesses acknowledge the trend, not all of them are equally prepared to meet the challenge. The companies in the best position to compete have transformed themselves into “data-driven” organizations.

Data-driven organizations routinely use data to inform strategy and decision-making. Although other businesses share the same goal, many of them are still struggling to build the necessary technological capabilities, or otherwise their culture is interfering with their ability to use data, or both.

Becoming a data-driven organization isn’t easy, however. In fact, it’s very difficult. While all organizations have a glut of data, their abilities to collect it, cleanse it, integrate it, manage it, access it, secure it, govern it, and analyze it vary significantly from company to company. Even though each of these factors helps ensure that data can be used with higher levels of confidence, it’s difficult for a business to realize the value of its data if its corporate culture lags behind its technological capabilities.

Data-driven organizations have extended the use of data across everyday business functions, from the C-suite to the front lines. Rather than hoping that executives, managers, and employees will use business intelligence (BI) and other analytical tools, companies that are serious about the use of data are training employees, making the systems easier to use, making it mandatory to use the systems, and monitoring the use of the systems. Because their ability to compete effectively depends on their ability to leverage data, such data-driven organizations make a point of aligning their values, goals, and strategies with their ability to execute.

On the following pages we reveal the six traits common to data-driven organizations that make them stand out from their competitors.

Forward Thinkers

Data-driven enterprises consider where they are, where they want to go, and how they want to get there. To ensure progress, they establish KPIs to monitor the success of business operations, departments, projects, employees, and initiatives. Quite often, these organizations have also established one or more cross-functional committees of decision-makers who collectively ensure that business goals, company practices, and technology implementations are in sync.

“The companies that have integrated data into their business strategies see it as a means of growing their businesses. They use it to differentiate themselves by providing customers with better service, quicker turnaround, and other things that the competition can’t meet,” said Ken Gilbert, director of business analytics at the University of Tennessee’s Office of Research and Economic Development, in an interview with InformationWeek. “They’re focused on the long-term and big-picture objectives, rather than tactical objectives.”

Uncovering Opportunities

Enterprises have been embracing BI and big data analytics with the goal of making better decisions faster. While that goal remains important to data-driven enterprises, they also are trying to uncover risks and opportunities that may not have been discoverable previously, either because they didn’t know what questions to ask or because previously used technology lacked the capability.

According to Gartner research VP Frank Buytendijk, fewer than half of big data projects focus on direct decision-making. Other objectives include marketing and sales growth, operational and financial performance improvement, risk and compliance management, new product and service innovation, and direct or indirect data monetization.

Hypothesis Trumps Assumption

People have been querying databases for decades to get answers to known questions. The shortcoming of that approach is assuming that the question asked is the optimal question to ask.

Data-driven businesses aim to continuously improve the quality of the questions they ask. Some of them also try to discover, through machine learning or other means, what questions they should be asking that they have not yet asked.

The desire to explore data is also reflected in the high demand for interactive self-service capabilities that enable users to adjust their thinking and their approaches in an iterative fashion.

Pervasive Analytics

Data analytics has completely transformed the way marketing departments operate. More departments than ever are using BI and other forms of analytics to improve business process efficiencies, reduce costs, improve operational performance, and increase customer satisfaction. A person’s role in the company influences how the data is used.

Big data and analytics are now on the agendas of boards of directors, which means that executives not only have to accept and support the use of the technologies, they also have to use them — meaning they have to lead by example. Aberdeen’s 2014 Business Analytics survey indicated that data-driven organizations are 63% more likely than the average organization to have “strong” or “highly pervasive” adoption of advanced analytical capabilities among corporate management.

Failure Is Acceptable

Some companies encourage employees to experiment because they want to fuel innovation. With experimentation comes some level of failure, which progressive companies are willing to accept within a given range.

Encouraging exploration and accepting the risk of failure that accompanies it can be difficult cultural adjustments, since failure is generally considered the opposite of success. Many organizations have made significant investments in big data, analytics, and BI solutions. Yet, some hesitate to encourage data experimentation among those who are not data scientists or business analysts. This is often because, historically, the company’s culture has encouraged conformity rather than original thinking. Such a mindset not only discourages innovation, it fails to acknowledge that the failure to take risks may be more dangerous than risking failure.

Data Scientists And Machine Learning

Data-driven companies often hire data scientists and use machine learning so they can continuously improve their ability to compete. Microsoft, IBM, Accenture, Google, and Amazon ranked first through fifth, respectively, in a recent list of 7,500 companies hiring data scientists. Google, Netflix, Amazon, Pandora, and PayPal are a few examples of companies using machine learning with the goal of developing deeper, longer-lasting, and more profitable relationships with their customers than previously possible.

Tech Buying: 6 Reasons Why IT Still Matters

ErrorOriginally published in InformationWeek, and available as a slideshow here.

Making major tech purchases, especially big data analytics and business intelligence tools, without consulting IT may cause major problems. Here’s why.

Although shadow IT is not new, the percentage of business tech purchases made outside IT is significant and growing. When Bain & Company conducted in-depth interviews with 67 marketing, customer service, and supply chain professionals in February 2014, it found that nearly one-third of technology purchasing power had moved to executives outside of IT. Similarly, member-based advisory firm CEB has estimated that non-IT departments control 30% of enterprise IT spend. By 2020, Gartner estimates, 90% of tech spending will occur outside IT.

There are many justifications for leaving IT in the dark about departmental tech purchases. For one thing, departmental technology budgets seem to point to departmental decision making. Meanwhile, cloud-based solutions, including analytics services, have become more popular with business users because they are easy to set up. In addition, their relatively low subscription rates or pay-per-use models may be more attractive from a budgetary standpoint than their traditional on-premises counterparts, which require significant upfront investments and IT consideration. Since the cost and onboarding barriers to cloud service adoption are generally lower than for on-premises products, IT’s involvement may seem to be unnecessary.

Besides, IT is busy. Enterprise environments are increasingly complex, and IT budgets are not growing proportionally, so the IT department is resource-constrained. Rather than waiting for IT — or complicating decision-making by getting others involved — non-IT tech buyers anxious to deploy a solution may be tempted to act first and answer questions later.

However, making tech purchase without IT’s involvement may result in unforeseen problems. On the following pages, we reveal six risks associated with making business tech purchases without involving IT.

1. Tech Purchases Affect Everybody
Tech purchases made without IT’s involvement may affect IT and the IT ecosystem in ways that someone outside IT couldn’t anticipate. You might be introducing technical risk factors or tapping IT resources IT will have to troubleshoot after the fact. To minimize the potential of unforeseen risks, IT can perform an in-depth assessment of your department’s requirements, the technology options, their trade-offs, and the potential ripple effect that your tech purchase might have across the organization. This kind of risk/benefit analysis is important. Even if it seems like a barrier for your department to get what it wants, it’s better for the entire organization in the long run.
Also, you may need help connecting to data sources, integrating data sources, and ensuring the quality of data, all of which require specific expertise. IT can help you understand the scope of an implementation in greater detail than you might readily see.

2. Sensitive Information May Be Compromised
Information security policies need to be defined, monitored, and enforced. While it’s common for businesses to have security policies in place, education about those policies, and the enforcement of those policies, sometimes fall short. Without appropriate precautions, security leaks can happen innocently, or you could be opening the door to intentional bad actors.
Cloud-based services can expose organizations to risks that users haven’t considered, especially when the service’s terms of use are not understood. Asurvey of 4,140 business and IT managers, conducted in July 2012 by The Ponemon Institute and sponsored by Thales e-Security, revealed that 63% of respondents did not know what cloud providers are doing to protect their sensitive or confidential data.

3. Faulty Data = Erroneous Conclusions
There is no shortage of data to analyze. However, inadequate data quality and access to only a subset of information can negatively impact the accuracy of analytics and, ultimately, decision making.
In an interview with InformationWeek, Jim Sterne, founder of the eMetrics Summit and the Digital Analytics Association, warned that the relative reliability of sources needs to be considered since CRM system data, onsite user behavior data, and social media sentiment analysis data are not equally trustworthy.
“If I’m looking at a dashboard as a senior executive and I know where the data came from and how it was cleansed and blended, I’m looking at the numbers as if they have equal weight,” he said. “It’s like opening up a spice cabinet and assuming each spice is as spicy as any other. I will make bad decisions because I don’t know how the information was derived.”

4. Not Getting What You Bought
Similar products often sound alike, but their actual capabilities can vary greatly. IT can help identify important differences.
While it may be tempting to purchase a product based on its exhaustive feature set or its latest enhancements, feature-based buying often proves to be a mistake because it omits or minimizes strategic thinking. To reduce the risk of buyer’s remorse, consulting with IT can help you assess your current and future requirements and help you choose a solution that aligns with your needs.

5. Scope Creep
Business users typically want immediate benefits from big data, analytics packages, and BI systems. But, if the project has a lot of technological complexity — and particularly if it involves tech dependencies that are outside the control of your department — it’s often best to implement in phases. Approaching large initiatives as one big project may prove to be more complicated, time-consuming, and costly than anticipated.
IT can help you break a large, difficult-to-manage project into several smaller projects, each of which has its own timeline and goals. That way, you can set realistic end-user and C-suite expectations and effectively control risks. Phasing large projects can also provide you with the flexibility you need to adjust your implementation as business requires.

6. Missing Out On Prior Experience
IT professionals and outsourced IT resources often have prior experience with BI and analytics implementations that are specific or relevant to your department. Some of them have implemented solutions in other companies, departments, or industries and have gained valuable insight from those experiences. When armed with such knowledge, they can help you understand potential opportunities, challenges, and pitfalls you may not have considered which can affect planning, implementation, and the choice of solutions.