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Tag: BI

How to Modernize Business Intelligence for Self-Service

One thing that drives analytics consultants absolutely batty is clients who believe their data is inherently clean, regardless. That’s on the business side, of course. IT, data science, and data engineers are all too familiar with what it takes to get data in order, but one thing even they may overlook is the usefulness of that data over the long term. Some are throwing everything into a data lake hoping for the best later, which is fine, as long as there’s some structure and governance in place.

GPU manufacturer NVIDIA is addressing that very problem as it attempts to enable self-service analytics. If the analytics are going to be reliable, then data quality and documentation need to be considered. As I write this, there’s a pilot project unfolding, and a big part of the effort is focused on the data itself.

“There’s not enough emphasis on data assets. It’s more like a byproduct of your systems,” said Ivan Chen, director of Enterprise Business Analytics at NVIDIA. “The first we’re doing is taking inventory of all our data assets, documenting them, and then trying to figure out how we can use that data effectively to make decisions and understand what it means.”

As part of the project, Chen and his team are documenting all the steps needed to meet a performance metric, so everyone can agree not only to the KPI but how that it is achieved.

“I want enable analysis, not give you a debatable report because neither of us really understands the nuances of the data,” said Chen. “If we define and document everything, then we can agree about what a data field is and how it’s used. That way, we can have a conversation about what to do with the data instead of debating about what wasn’t considered.”

The scope of the documentation includes memorializing the data transformation so people can understand how it was done now and later.

Modernizing the BI Platform

NVIDIA is in the process of modernizing its BI platform to enable self-service capabilities. Like other companies, NVIDIA has struggled to leverage pockets of data owned by different people, even though the data came from the same source, such as an ERP system.

“What we’re doing now is we’re bringing it all together in a central repository and documenting all that, so analysts can use it in a self-service way,” said Chen. “I’d like to scale our documentation that so more people understand what’s available and how they can use it, because we’ll be able to create more insights.”

The effort is an attempt to overcome what so many organizations have experienced which is business requirements that move and change at an Agile pace, and the Waterfall nature of IT creating reports, which is gathering requirements, building something, and then finding out whether it really meets the needs of the business or not.

“NVIDIA has a very progressive IT shop where they want to partner with and help business,” said Chen. “As part of the three-month trial, we’re testing the Agile method. There’s a dedicated team from IT that’s solely working on this so there is some sustainability.”

Part of working with IT is resolving the Agile-Waterfall disconnects because the point of self-service analytics is to enable faster and more timely insights.

“Analytics really needs to be treated differently than an implementation project,” said Chen. “I think IT has accommodated this because they recognize that Waterfall reporting is not going to work in today’s fast-changing environment. That’s why we’re able to try this new [Agile] method.”

If everything goes well, the project will scale up to the enterprise level eventually, which will enable more and different types of insights than are achievable today.

Achieving a holistic view is very important if you want to answer important cross-functional questions,” said Chen. “There are a lot of connection points that need to be understood and reconciled if you want to get to an accurate, holistic view. Most companies don’t have that yet.”

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.