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When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and difficult execution of strategy. Employing data quality best practices presents a terrific opportunity to improve business performance.

The Unmeasured Costs of Bad Customer and Prospect Data

Perhaps Thomas Redman’s most important recent article is “Seizing Opportunity in Data Quality.”  Sloan Management Review published it in November 2017, and it appears below.  Here he expands on the “unmeasured” and “unmeasurable” costs of bad data, particularly in the context of customer data, and why companies need to initiate data quality strategies.

Here is the article, reprinted in its entirety with permission from Sloan Management Review.

The cost of bad data is an astonishing 15% to 25% of revenue for most companies.

Getting in front on data quality presents a terrific opportunity to improve business performance. Better data means fewer mistakes, lower costs, better decisions, and better products. Further, I predict that many companies that don’t give data quality its due will struggle to survive in the business environment of the future.

Bad data is the norm. Every day, businesses send packages to customers, managers decide which candidate to hire, and executives make long-term plans based on data provided by others. When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and added difficulties in the execution of strategy. You know the sound bites — “decisions are no better than the data on which they’re based” and “garbage in, garbage out.” But do you know the price tag to your organization?

Based on recent research by Experian plc, as well as by consultants James Price of Experience Matters and Martin Spratt of Clear Strategic IT Partners Pty. Ltd., we estimate the cost of bad data to be 15% to 25% of revenue for most companies (more on this research later). These costs come as people accommodate bad data by correcting errors, seeking confirmation from other sources, and dealing with the inevitable mistakes that follow.

Fewer errors mean lower costs, and the key to fewer errors lies in finding and eliminating their root causes. Fortunately, this is not too difficult in most cases. All told, we estimate that two-thirds of these costs can be identified and eliminated — permanently.

In the past, I could understand a company’s lack of attention to data quality because the business case seemed complex, disjointed, and incomplete. But recent work fills important gaps.

The case builds on four interrelated components: the current state of data quality, the immediate consequences of bad data, the associated costs, and the benefits of getting in front on data quality. Let’s consider each in turn.

Four Reasons to Pay Attention to Data Quality Now

The Current Level of Data Quality Is Extremely Low

A new study that I recently completed with Tadhg Nagle and Dave Sammon (both of Cork University Business School) looked at data quality levels in actual practice and shows just how terrible the situation is.

A new study that I recently completed with Tadhg Nagle and Dave Sammon (both of Cork University Business School) looked at data quality levels in actual practice and shows just how terrible the situation is.

We had 75 executives identify the last 100 units of work their departments had done — essentially 100 data records — and then review that work’s quality. Only 3% of the collections fell within the “acceptable” range of error. Nearly 50% of newly created data records had critical errors.

Said differently, the vast majority of data is simply unacceptable, and much of it is atrocious. Unless you have hard evidence to the contrary, you must assume that your data is in similar shape.

Bad Data Has Immediate Consequences

Virtually everyone, at every level, agrees that high-quality data is critical to their work. Many people go to great lengths to check data, seeking confirmation from secondary sources and making corrections. These efforts constitute what I call “hidden data factories” and reflect a reactive approach to data quality. Accommodating bad data this way wastes time, is expensive, and doesn’t work well. Even worse, the underlying problems that created the bad data never go away.

One consequence is that knowledge workers waste up to 50% of their time dealing with mundane data quality issues. For data scientists, this number may go as high as 80%.

A second consequence is mistakes, errors in operations, bad decisions, bad analytics, and bad algorithms. Indeed, “big garbage in, big garbage out” is the new “garbage in, garbage out.”

Finally, bad data erodes trust. In fact, only 16% of managers fully trust the data they use to make important decisions.

Frankly, given the quality levels noted above, it is a wonder that anyone trusts any data.

When Totaled, the Business Costs Are Enormous

Obviously, the errors, wasted time, and lack of trust that are bred by bad data come at high costs.

Companies throw away 20% of their revenue dealing with data quality issues. This figure synthesizes estimates provided by Experian (worldwide, bad data cost companies 23% of revenue), Price of Experience Matters ($20,000/employee cost to bad data), and Spratt of Clear Strategic IT Partners (16% to 32% wasted effort dealing with data). The total cost to the U.S. economy: an estimated $3.1 trillion per year, according to IBM.

The costs to businesses of angry customers and bad decisions resulting from bad data are immeasurable — but enormous.

Finally, it is much more difficult to become data-driven when a company can’t depend on its data. In the data space, everything begins and ends with quality. You can’t expect to make much of a business selling or licensing bad data. You should not trust analytics if you don’t trust the data. And you can’t expect people to use data they don’t trust when making decisions.

Two-Thirds of These Costs Can Be Eliminated by Getting in Front on Data Quality

“Getting in front on data quality” stands in contrast to the reactive approach most companies take today. It involves attacking data quality proactively by searching out and eliminating the root causes of errors. To be clear, this is about management, not technology — data quality is a business problem, not an IT problem.

Companies that have invested in fixing the sources of poor data — including AT&T, Royal Dutch Shell, Chevron, and Morningstar — have found great success. They lead us to conclude that the root causes of 80% or more of errors can be eliminated; that up to two-thirds of the measurable costs can be permanently eliminated; and that trust improves as the data does.

Which Companies Should Be Addressing Data Quality?

While attacking data quality is important for all, it carries a special urgency for four kinds of companies and government agencies:

Those that must keep an eye on costs. Examples include retailers, especially those competing with Amazon.com Inc.; oil and gas companies, which have seen prices cut in half in the past four years; government agencies, tasked with doing more with less; and companies in health care, which simply must do a better job containing costs. Paring costs by purging the waste and hidden data factories created by bad data makes far more sense than indiscriminate layoffs — and strengthens a company in the process.

Those seeking to put their data to work. Companies include those that sell or license data, those seeking to monetize data, those deploying analytics more broadly, those experimenting with artificial intelligence, and those that want to digitize operations. Organizations can, of course, pursue such objectives using data loaded with errors, and many companies do. But the chances of success increase as the data improves.

Those unsure where primary responsibility for data should reside. Most businesspeople readily admit that data quality is a problem, but claim it is the province of IT. IT people also readily admit that data quality is an issue, but they claim it is the province of the business — and a sort of uneasy stasis results. It is time to put an end to this folly. Senior management must assign primary responsibility for data to the business.

Those who are simply sick and tired of making decisions using data they don’t trust. Better data means better decisions with less stress. Better data also frees up time to focus on the really important and complex decisions.

Next Steps for Senior Executives

In my experience, many executives find reasons to discount or even dismiss the bad news about bad data. Common refrains include, “The numbers seem too big, they can’t be right,” and “I’ve been in this business 20 years, and trust me, our data is as good as it can be,” and “It’s my job to make the best possible call even in the face of bad data.”

But I encourage each executive to think deeply about the implications of these statistics for his or her own company, department, or agency, and then develop a business case for tackling the problem. Senior executives must explore the implications of data quality given their own unique markets, capabilities, and challenges.

The first step is to connect the organization or department’s most important business objectives to data. Which decisions and activities and goals depend on what kinds of data?

The second step is to establish a data quality baseline. I find that many executives make this step overly complex. A simple process is to select one of the activities identified in the first step — such as setting up a customer account or delivering a product — and then do a quick quality review of the last 100 times the organization did that activity. I call this the Friday Afternoon Measurement because it can be done with a small team in an hour or two.

The third step is to estimate the consequences and their costs for bad data. Again, keep the focus narrow — managers who need to keep an eye on costs should concentrate on hidden data factories; those focusing on AI can concentrate on wasted time and the increased risk of failure; and so forth.

Finally, for the fourth step, estimate the benefits — cost savings, lower risk, better decisions — that your organization will reap if you can eliminate 80% of the most common errors. These form your targets going forward.

Chances are that after your organization sees the improvements generated by only the first few projects, it will find far more opportunity in data quality than it had thought possible. And if you move quickly, while bad data is still the norm, you may also find an unexpected opportunity to put some distance between yourself and your competitors.

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Service Objects spoke with the author, Tom Redman, and he gave us an update on the Sloan Management article reprinted above, particularly as it relates to the subject of the costs associated with bad customer data.

Please focus first on the measurable costs of bad customer data.  Included are items such as the cost of the work Sales does to fix up bad prospect data it receives from Marketing, the costs of making good for a customer when Operations sends him or her the wrong stuff, and the cost of work needed to get the various systems which house customer data to “talk.”  These costs are enormous.  For all data, it amounts to roughly twenty percent of revenue.

But how about these costs:

  • The revenue lost when a prospect doesn’t get your flyer because you mailed it to the wrong address.
  • The revenue lost when a customer quits buying from you because fixing a billing problem was such a chore.
  • The additional revenue lost when he/she tells a friend about his or her experiences.

This list could go on and on.

Most items involve lost revenue and, unfortunately, we don’t know how to estimate “sales you would have made.”  But they do call to mind similar unmeasurable costs associated with poor manufacturing in the 1970s and 80s.  While expert opinion varied, a good first estimate was that the unmeasured costs roughly equaled the measured costs.

If the added costs in the Seizing Opportunity article above doesn’t scare into action, add in a similar estimate for lost revenue.

The only recourse is to professionally manage the quality of prospect and customer data.  It is not hyperbole to note that such data are among a company’s most important assets and demand no less.

©2018, Data Quality Solutions

 

Study after study has shown that investing in employee experience impacts the customer experience and can generate a high ROI for the company.

The Un-Ignorable Link Between Employee Experience And Customer Experience

Engaged employees lead to happy customers.

There is an undeniable link between employee experience and customer experience. Companies that lead in customer experience have 60% more engaged employees, and study after study has shown that investing in employee experience impacts the customer experience and can generate a high ROI for the company. Here are 10 companies that have seen the benefit of engaging their employees to build customer experience.

“Take care of associates and they’ll take care of your customers.” -J.W.Marriott

Marriott International founder J.W. Marriott said, “Take care of associates and they’ll take care of your customers.” It still holds true at the company—employees are valued, which makes them want to share that experience with guests. Marriott publicly rewards employees for a job well done, celebrates diversity and inclusion, values loyalty, and offers a wide variety of training programs. It has been regularly rated a top place to work and a top company for customer experience.

Chick-Fil-A Encourages Employees To Build Relationships With Customers

With its chicken and waffle fries, Chick-fil-A generates more revenue per restaurant than any other chain in the country. But it’s not just the food that sets the restaurant apart—it’s the employees. Franchise owners are given thorough training but also have bandwidth to explore creative ideas. Employees are encouraged to build relationships with customers because they have strong relationships with each other and with the company.

The Zappos Contact Center Calls Its Team Customer Loyalty Team Members

E-commerce site Zappos is known for connecting with its customers and for responding to issues quickly. That’s likely because the company also has a great reputation for connecting with its employees. Every employee plays a role in the company’s customer-first culture—even call center employees are referred to as customer loyalty team members. When employees feel connected to and valued by the brand, they want to bring customers into the circle.

Nordstrom Only Asks Employees To Use Their Best Judgement

Employees at Nordstrom are given just one rule in their employee handbook: “Use best judgment in all situations. There will be no additional rules.” Instead of being bogged down with corporate guidance, empowered employees know they are trusted and valued. That translates to their interactions with customers and is a large reason why the “Nordstrom Way” of doing customer service is well respected.

Taco Bell Provides An Easy Way For Employees To Ask For Help

Fast food giant Taco Bell puts employees first by always providing them a way to contact management. The company has a network of 1-800 numbers to field complaints, answer questions, and alert management of potential red flags for its 175,000-plus employees. It also holds regular employee roundtable meetings and company-wide surveys to gage employee satisfaction. With their needs met and questions answered, employees can focus on helping customers.

Jet Blue Employees Are Allowed To Go The Extra Mile For Customers

Jet Blue is consistently rated one of the best airlines, and a large part of that is the great customer experience. Jet Blue’s employees are given the freedom to go the extra mile to help customers. Instead of being constrained by red tape and bureaucracy, employees have power to solve problems themselves, which means they often consider customers problems to be their own. Jet Blue also fosters a spirit of collaboration and teamwork with employees that extends to customers.

Starbucks Provides Extensive Training On How To Interact With Customers

Starbucks knows that happy employees lead to happy customers. The company is consistently at the top of every customer experience “best” list, and this recognition comes from taking care of its employees. Starbucks provides employees competitive wages, health benefits, and stock options. Each employee is trained not only on how to make the drinks but also how to interact with customers. The welcoming atmosphere of a Starbucks coffee shop is echoed in the company, where every employee knows they are welcomed and included.

Airbnb Helps Employees Focus On Personal Growth

Airbnb’s mission statement of “Belong Anywhere” extends beyond customers to also include employees. Airbnb is invested in every aspect of its employees’ lives, not just what they do at the office. The company works to create a culture that sets employees up for success in their personal and professional lives, from having a flexible, open office space to being transparent with the goals of the company. Employees can focus on their personal growth and the mission of the company, which allows them to create better customer experiences.

Adobe Ties Employee Compensation To Customer Experience

Instead of viewing customers and employees as separate entities, Adobe brings them together to drive positive, connected experiences. Employees are trained on customer experience metrics and how each person’s role impacts the overall customer experience. It also encourages employees to be advocates for customers’ needs and jump in when they see a problem instead of waiting for something to run its course. At Adobe, employee compensation is tied to customer experience. When employees are connected with customers and see the role they can each play individually, they want to create a better experience (disclosure: Adobe is a client).

GE Uses Root Cause Analysis To Improve Customer Satisfaction

It takes an innovative HR department to drive employee experience at General Electric. Employees are involved in the process to make sure they have the physical space and technological tools to do their best work and that training programs keep employees moving forward. When a division of GE saw it had low customer satisfaction scores, it worked to find the root cause and streamline internal processes. Cutting red tape keeps employees happier and allows them to be more productive, which helped the customer satisfaction score jump more than 40% in two years.

Your employees are your often your most untapped resource when it comes to building powerful customer experiences. I hope you are just as inspired by the companies highlighted here as I was.

The article above was first published on Forbes.com and reprinted with permission. View original post here.

About the author: Blake Morgan is a customer experience futurist, author of More Is More, and keynote speaker. You can read more of Blake’s articles by visiting her website.

A Daisy Chain of Hidden Customer Data Factories

I published the provocatively-titled article, Bad Data Costs the United States $3 Trillion per Year in September, 2016 at Harvard Business Review. It is of special importance to those who need prospect/customer/contact data in the course of their work.

First read the article.

Consider this figure: $136 billion per year. That’s the research firm IDC’s estimate of the size of the big data market, worldwide, in 2016. This figure should surprise no one with an interest in big data.

But here’s another number: $3.1 trillion, IBM’s estimate of the yearly cost of poor quality data, in the US alone, in 2016. While most people who deal in data every day know that bad data is costly, this figure stuns.

While the numbers are not really comparable, and there is considerable variation around each, one can only conclude that right now, improving data quality represents the far larger data opportunity. Leaders are well-advised to develop a deeper appreciation for the opportunities improving data quality present and take fuller advantage than they do today.

The reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive. The data they need has plenty of errors, and in the face of a critical deadline, many individuals simply make corrections themselves to complete the task at hand. They don’t think to reach out to the data creator, explain their requirements, and help eliminate root causes.

Quite quickly, this business of checking the data and making corrections becomes just another fact of work life.  Take a look at the figure below. Department B, in addition to doing its own work, must add steps to accommodate errors created by Department A. It corrects most errors, though some leak through to customers. Thus Department B must also deal with the consequences of those errors that leak through, which may include such issues as angry customers (and bosses!), packages sent to the wrong address, and requests for lower invoices.

The Hidden Data Factory

Visualizing the extra steps required to correct the costly and time consuming data errors.

I call the added steps the “hidden data factory.” Companies, government agencies, and other organizations are rife with hidden data factories. Salespeople waste time dealing with erred prospect data; service delivery people waste time correcting flawed customer orders received from sales. Data scientists spend an inordinate amount of time cleaning data; IT expends enormous effort lining up systems that “don’t talk.” Senior executives hedge their plans because they don’t trust the numbers from finance.

Such hidden data factories are expensive. They form the basis for IBM’s $3.1 trillion per year figure. But quite naturally, managers should be more interested in the costs to their own organizations than to the economy as a whole. So consider:

There is no mystery in reducing the costs of bad data — you have to shine a harsh light on those hidden data factories and reduce them as much as possible. The aforementioned Friday Afternoon Measurement and the rule of ten help shine that harsh light. So too does the realization that hidden data factories represent non-value-added work.

To see this, look once more at the process above. If Department A does its work well, then Department B would not need to handle the added steps of finding, correcting, and dealing with the consequences of errors, obviating the need for the hidden factory. No reasonably well-informed external customer would pay more for these steps. Thus, the hidden data factory creates no value. By taking steps to remove these inefficiencies, you can spend more time on the more valuable work they will pay for.

Note that very near term, you probably have to continue to do this work. It is simply irresponsible to use bad data or pass it onto a customer. At the same time, all good managers know that, they must minimize such work.

It is clear enough that the way to reduce the size of the hidden data factories is to quit making so many errors. In the two-step process above, this means that Department B must reach out to Department A, explain its requirements, cite some example errors, and share measurements. Department A, for its part, must acknowledge that it is the source of added cost to Department B and work diligently to find and eliminate the root causes of error. Those that follow this regimen almost always reduce the costs associated with hidden data factories by two thirds and often by 90% or more.

I don’t want to make this sound simpler than it really is. It requires a new way of thinking. Sorting out your requirements as a customer can take some effort, it is not always clear where the data originate, and there is the occasional root cause that is tough to resolve. Still, the vast majority of data quality issues yield.

Importantly, the benefits of improving data quality go far beyond reduced costs. It is hard to imagine any sort of future in data when so much is so bad. Thus, improving data quality is a gift that keeps giving — it enables you to take out costs permanently and to more easily pursue other data strategies. For all but a few, there is no better opportunity in data.

The article above was originally written for Harvard Business Review and is reprinted with permission.
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In January 2018, Service Objects spoke with the author, Tom Redman, and he gave us an update on the article above, particularly as it relates to the subject of data quality.

According to Tom, the original article anticipated people asking, “What’s going on?  Don’t people care about data quality?”

The answer is, “Of course they care.  A lot.  So much that they implement ‘hidden data factories’ to accommodate bad data so they can do their work.”  And the article explored such factories in a generic “two-department” scenario.

Of course, hidden data factories take a lot of time and cost a lot of money, both contributing to the $3T/year figure.  They also don’t work very well, allowing lots of errors to creep through, leading to another hidden data factory.  And another and another, forming a sort of “daisy chain” of hidden data factories.  Thus, when one extends the figure above and narrows the focus to customer data, one gets something like this:

I hope readers see the essential truth this picture conveys and are appalled.  Companies must get in front on data quality and make these hidden data factories go away!

©2018, Data Quality Solutions

Improving Customer Satisfaction Through Data Quality

“Online retailers of all sizes are constantly under attack by sophisticated fraudsters. In fact, credit card fraud costs US online retailers an estimated $3.9 billion each year.” – Geoff Grow, Founder and CEO, Service Objects

At Service Objects, we know that data quality excellence is the key to helping retailers feel confident about improving delivery rates while reducing fraud associated with vacant addresses, PO boxes and commercial mail handlers. This, in turn, helps maintain higher customer satisfaction ratings among your legitimate customers.

This video, featuring Service Objects’ Founder and CEO, Geoff Grow, will show you tools you can use to improve the deliverability of your products and combat fraud. You will learn how to validate addresses against current USPS certified address data to prevent undeliverable and lost shipments, as well as how to validate a customer’s IP address against the billing and shipping information they provide, using data from over many authoritative data sources to stop fraud before it happens.

 

To Be Customer-Centric, You Have To Be Data-Centric

In today’s fast-paced world, customers have become more demanding than ever before. Customer-centric organizations need to build their models after critically analyzing their customers, and this requires them to be data-centric.

Today, customers expect companies to be available 24/7 to solve their queries. They expect companies to provide them with seamless information about their products and services. Not getting such features can have a serious impact on their buying decision. Customer-centric organizations need to adopt a data-centric approach not just for targeted customer interactions, but also to survive competition from peers who are data-centric.

Customer-centric organizations need to go data-centric

Why?

Today, customers enquire a lot before making a decision. And social media enquiries are the most widely used by customers. A satisfactory experience is not limited to prompt answers to customer queries alone. Customers need a good experience before making the purchase, during the installment of product or deployment of service, and even after making the purchase. Thus, to retain a customer’s loyalty and to drive in regular profits, companies need to be customer-centric. And that can only happen if companies adopt a data-centric approach. Big data comes handy in developing a model that gives optimum customer experience. It helps build a profound understanding of the customer, such as what they like and value the most and the customer lifetime value to the company. Besides, every department in a data-centric organization can have the same view about its customer, which guarantees efficient customer interactions. Companies like Amazon and Zappos are the best examples of customer-centric organizations that heavily depend on data to provide a unique experience to their customers. This has clearly helped them come a long way.

How?

Companies can collect a lot of information that can help them become customer-centric. Here are some ways in which they can do so:

  • Keep a close eye on any kind of new data that could help them stay competitive, such as their product prices and the money they invest in logistics and in-product promotion. They need to constantly monitor the data that tells them about the drivers of these income sources.
  • Reach out to customers from different fields with varying skill sets to derive as many insights as possible so as to help make a better product.
  • Develop a full-scale data project that will help them collect data and apply it to make a good data strategy and develop a successful business case.

Today, there is no escaping customer expectations. Companies need to satisfy customers for repeat business. Customer satisfaction is the backbone of selling products and services, maintaining a steady flow of revenue, and for the certainty of business. And for all of that to happen, companies need to gather and democratize as much information about their customers as possible.

Reprinted with permission from the author. View original post here.

Author: Naveen Joshi, Founder and CEO of Allerin

The Difference Between Customer Experience And User Experience

There are a lot of buzzwords thrown around in the customer sphere, but two of the big ones relate to experiences—customer and user. Although CX and UX are different and unique, they must work together for a company to have success.

User experience deals with customers’ interaction with a product, website, or app. It is measured in things like abandonment rate, error rate, and clicks to completion. Essentially, if a product or technology is difficult to use or navigate, it has a poor user experience.

Customer experience on the other hand focuses on the general experience a customer has with a company. It tends to exist higher in the clouds and can involve a number of interactions. It is measured by net promoter score, customer loyalty, and customer satisfaction.

Both customer experience and user experience are incredibly important and can’t truly exist and thrive without each other. If a website or mobile app has a bad layout and is cumbersome to navigate, it will be difficult for customers to find what they need and can lead to frustration. If customers can’t easily open the mobile app from an email on their phone, they likely won’t purchase your product. Likewise, if the product layout is clunky, customers likely won’t recommend it to a friend no matter how innovative it is. User experience is a huge part of customer experience and needs to play a major role when thinking like a customer.

Although UX and CX are different, they need to work closely together to truly be successful. Customer experience representatives should be working alongside product engineers to make sure everything works together. By taking themselves through the entire customer journey, they can see how each role plays into a customer’s overall satisfaction with the product and the company. The ultimate goal is a website or product that beautifully meshes the required elements of navigation and ease with the extra features that will help the brand stand out with customers.

When thinking about customer experience, user experience definitely shouldn’t be left behind. Make both unique features an essential part of your customer plan to build a brand that customers love all around.

Reprinted with permission from the author. View original post here.

Author Bio: Blake Morgan is a customer experience futurist, author of More Is More, and keynote speaker.

Go farther and create knock your socks-off customer experiences in your organization by enrolling in her new Customer Experience School.

The Talent Gap In Data Analytics

According to a recent blog by Villanova University, the amount of data generated annually has grown tremendously over the last two decades due to increased web connectivity, as well as the ever-growing popularity of internet-enabled mobile devices. Some organizations have found it difficult to take advantage of the data at their disposal due to a shortage of data-analytics experts. Primarily, small-to-medium enterprises (SMBs) who struggle to match the salaries offered by larger businesses are the most affected. This shortage of qualified and experienced professionals is creating a unique opportunity for those looking to break into a data-analysis career.

Below is some more information on this topic.

Data-Analytics Career Outlook

Job openings for computer and research scientists are expected to grow by 11 percent from 2014 to 2024. In comparison, job openings for all occupations are projected to grow by 7 percent over the same period. Besides this, 82 percent of organizations in the US say that they are planning to advertise positions that require data-analytics expertise. This is in addition to 72 percent of organizations that have already hired talent to fill open analytics positions in the last year. However, up to 78 percent of businesses say they have experienced challenges filling open data-analytics positions over the last 12 months.

Data-Analytics Skills

The skills that data scientists require vary depending on the nature of data to be analyzed as well as the scale and scope of analytical work. Nevertheless, analytics experts require a wide range of skills to excel. For starters, data scientists say they spend up to 60 percent of their time cleaning and aggregating data. This is necessary because most of the data that organizations collect is unstructured and comes from diverse sources. Making sense of such data is challenging, because the majority of modern databases and data-analytics tools only support structured data. Besides this, data scientists spend at least 19 percent of their time collecting data sets from different sources.

Common Job Responsibilities

To start with, 69 percent of data scientists perform exploratory data-analytics tasks, which in turn form the basis for more in-depth querying. Moreover, 61 percent perform analytics with the aim of answering specific questions, 58 percent are expected to deliver actionable insights to decision-makers, and 53 percent undertake data cleaning. Additionally, 49 percent are tasked with creating data visualizations, 47 percent leverage data wrangling to identify problems that can be resolved via data-driven processes, and 43 percent perform feature extraction, while 43 percent have the responsibility of developing data-based prototype models.

In-demand Programming-Language Skills

In-depth understanding of SQL is a key requirement cited in 56 percent of job listings for data scientists. Other leading programming-language skills include Hadoop (49 percent of job listings), Python (39 percent), Java (36 percent), and R (32 percent).

The Big-Data Revolution

The big-data revolution witnessed in the last few years has changed the way businesses operate substantially. In fact, 78 percent of corporate organizations believe big data is likely to fundamentally change their operational style over the next three years, while 71 percent of businesses expect the same resource to spawn new revenue opportunities. Only 58 percent of executives believe that their employer has the capability to leverage the power of big data. Nevertheless, 53 percent of companies are planning to roll out data-driven initiatives in the next 12 months.

Recruiting Trends

Companies across all industries are facing a serious shortage of experienced data scientists, which means they risk losing business opportunities to firms that have found the right talent. Common responsibilities among these professionals include developing data visualizations, collecting data, cleaning and aggregating unstructured data, and delivering actionable insights to decision-makers. Leading employers include the financial services, marketing, corporate and technology industries.

View the full infographic created by Villanova University’s Online Master of Science in Analytics degree program.

http://taxandbusinessonline.villanova.edu/resources-business/infographic-business/the-talent-gap-in-data-analytics.html

Reprinted with permission.

What Can We Do? Service Objects Responds to Hurricane Harvey

The Service Objects’ team watched the steady stream of images from Hurricane Harvey and its aftermath and we wanted to know, ‘What can we do to help?’  We realized the best thing we could do is offer our expertise and services free to those who can make the most use of them – the emergency management agencies dedicated to helping those affected by this disaster.

It was quickly realized that as Hurricane Harvey continues to cause record floodwaters and entire neighborhoods are under water, these agencies are finding it nearly impossible to find specific addresses in need of critical assistance. In response to this, we are offering emergency management groups the ability to quickly pinpoint addresses with latitude and longitude coordinates by offering unlimited, no cost access to DOTS Address Geocode ℠ (AG-US). By using Address Geocode, the agencies will not have to rely on potentially incomplete online maps. Instead, using Service Objects’ advanced address mapping services, these agencies will be able to reliably identify specific longitude and latitude coordinates in real-time and service those in need.

“The fallout of the catastrophic floods in Texas is beyond description, and over one million locations in Houston alone have been affected,” said Geoff Grow, CEO and Founder of Service Objects.  “With more than 450,000 people likely to seek federal aid in recovering from this disaster, Service Objects is providing advanced address mapping to help emergency management agencies distribute recovery funds as quickly as possible. We are committed to helping those affected by Hurricane Harvey.”

In addition, as disaster relief efforts are getting underway, Service Objects will provide free access to our address validation products to enable emergency management agencies to quickly distribute recovery funds by address type, geoid, county, census-block and census-track. These data points are required by the federal government to release funding.  This will allow those starting the recovery process from this natural disaster to get next level services as soon as possible.

To get access to Service Objects address solutions or request maps, qualified agencies can contact Service Objects directly by calling 805-963-1700 or by emailing us at info@serviceobjects.com.

Our team wishes the best for all those affected by Hurricane Harvey.

Image by National Weather Service 

Ensuring Addresses are Accurate and Up-to-Date

“Did you know that nearly 30 million Americans move each year? Did you also know that government agencies like counties, cities, and states are required to keep accurate and up to date records of their private citizens for communication purposes?”

Service Objects is committed to helping businesses reduce waste, and identify and improve operating efficiency through data quality excellence. And according to founder and CEO Geoff Grow, you can do this using databases up-to-the-minute USPS-certified data and more to verify your contact records.

This video will show you how to use simple API and web-based tools that validate and append data to your contact information. You will learn how data quality solutions can:

  • identify change of addresses, making it easier to keep your contact records accurate and up-to-date,
  • validate addresses to maximize delivery rates,
  • geocode addresses to provide highly accurate latitude and longitude information. In addition,
  • and append census, ZIP code and county boundary data.