Taming The Big Data Beast

The future of big data has to do with the “Big Variety” problem or what Stonebraker likes to call “the 800-pound gorilla in the corner.” The problem dates back to the emergence of data warehouses in the 1990s and the need to “clean” and integrate the data coming from a number of data sources, making sure it conforms to a global data dictionary (e.g., “salary” in one data source is a synonym for “wages” in another). The process of data integration invented then, Extract-Transform-Load (ETL), is still used today.  But it doesn’t scale, argues Stonebraker, failing when you try to integrate data from thousands of data sources, increasingly a “business as usual” reality for many enterprises trying to tap the abundance of public sources now available on the Web, to say nothing of what’s to come with the emergence of the Internet of Things and the yet-to-emerge new-data-generating technology.

“The trouble with doing global upfront data models is that no one has figured out how to make them work,” says Stonebraker. “The only thing you can do is put the data together after the fact.” The solution is a mix of automated machine learning and the crowd sourcing of domain experts and the resultant startup, Tamr, was launched in 2013.

Despite businesses being told for years to abolish their data in order to thrive, award winner, Stonebraker says that we must instead preserve the “data silos”.  Stonebraker and his collaborators came up with a way to mask the data silos, super-imposing on them a layer of software that adapts to the constantly changing semantic environment of the organization, based on a human-computer collaborative process.

Preserving data silos is also about the future of the business. “Agility is going to be crucial to successful enterprises,” says Stonebraker, “and I don’t see a way to do that realistically without decomposing into independent business units. The minute you do that you either anoint a Chief Data Officer to keep everybody from diverging or you say, ‘look, run as fast as you can.’ I would err on the side of agility rather than standardization.”

Tamr is his “fourth attempt at doing data integration,” Stonebraker says, “and I think we finally got it right.” Not getting it right happens when you make it happen.  All of Stonebraker’s solutions involve some innovative take on known technological tradeoffs, all balanced against the cost of not just the technology but also the people and processes around it. The solution may not get one or more of the components right or will incur unforeseen costs when it is implemented in the real world. Or the timing could be off. “If you are too late, you are toast, if you are too early you are toast,” says Stonebraker. “There’s a lot of serendipity involved. You have to guess the market and lead it.”

Is there a future beyond the future defined by the three startups Stonebraker is currently involved with? “Right now I’m not interested in starting any more companies,” Stonebraker says flatly. But then he adds: “If I had more bandwidth, it would be what I’m working on at MIT right now, what we call Polystores.”

Again, this is an age-old problem, tackled before with the not-too-successful concept of “federated” databases. Today, it’s an extension and expansion (in my opinion) of the Big Variety problem, what happens after the data has been “curated” (cleaned and integrated). Following his strong convictions about the advantages of special-purpose databases and given the proliferation of not just sources of data but also data types, Stonebraker suggests that “it makes sense to load the curated data into multiple DBMSs. For example, the structured data into an RDBMS, the real-time data into a stream processing engine, the historical archive into an array engine, the text into Lucene, and the semi-structured data into a JSON system.”

Big Data’s future is really about simplifying the applications that are deployed over multiple, special-purpose database engines. “If your application is managing what you want to think of as a single database which is in fact spread over multiple engines,” says Stonebraker, “with different data models, different transaction systems, different everything, than you want a next-generation federation mechanism to make it as simple as possible to program.”

With Stonebraker’s “make it happen” attitude, we may just be one step closer to turning Big Data into Smart Data.

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New Procedures Reducing Installation Times and “No Shows” Result in Excess of $10 Million in Savings

Reliable Reports and Metrics Improve Customer Service

In the initial phases of the telephony over Hybrid Fiber Coax, one of the largest Multi-System Operators (MSO) in the broadband industry knew they had issues with both customer installations and customer satisfaction, but lacked the ability to identify the extent of the problems or the reasons behind them. The organization called upon Cliintel to develop a system to capture reliable, actionable data and to assist them with interpretation of that data.

The Business Issue:

The corporate telephony field operations group at the MSO needed a better understanding of the total installation process. This group was receiving conflicting information around the daily installs from disparate markets across the United States, each of which were performing thousands of installs per day. Reporting was not standardized, and the accuracy of information was very much in doubt.

To manage the situation, the MSO needed to:

• Identify which data was important to capture
• Design a reliable means to obtain the data
• Determine how to interpret the data

Management wanted to know the reasons for procedural problems during an installation, how long the installs were taking, and set a reasonable baseline a true time per task. Additionally, they wanted to find out the causes of the number of “no shows” for service appointments

As new procedures were adopted, installation times and “no shows” were reduced resulting in revenue generation in excess of $10M.

The Solution:

Cliintel worked with the MSO to identify and source all of the critical data points. This included creating Reason Codes to describe why an appointment was missed, cancelled or rescheduled. Additionally Cliintel resources created business rules for allocating technician drive time. Once the critical data elements were identified, an automated reporting system was developed and deployed. This system greatly reduced the “human error” element that had confounded earlier attempts at manually gathering data. Meetings were facilitated between the corporate and individual market groups to define the standards against which the data would be measured, such as: How long each type of job should take? The meetings were continued as reports were made available to the organization on a daily, weekly, monthly and quarterly basis. Charts, tables and graphs were utilized to help the organization understand the data. The meetings with the corporate and market groups continued to aid the organization in understanding what it was seeing and to develop strategies to deal with the identified problems.

The Results:

Trends were uncovered highlighting problems with provisioning efficiencies, installation times and customer satisfaction levels. These trends were identified in the reports and provided decision support and agnostic intelligence, actionable at the various levels within the organization. The publication of the results enabled the global installation standards team to identify new procedures to address the trends. As new procedures were adopted, installation times and “no shows” were reduced resulting in revenue generation in excess of $10M. A concurrent rise in customer satisfaction levels confirmed that the team had identified the important variables. This was a two-year project that left the MSO with a reporting tool that worked to help the organization understand and improve the daily installation process for the telephony product line across the enterprise.

We’re proud to help our clients solve tough problems and achieve stunning results. To see what kind of results Cliintel can deliver for you, please visit www.cliintel.com or e-mail askcliintel@cliintel.com.

Is Your Data Biased?

Leaders in business are handed extremely large amounts of data on their customer’s personal information. How do these business leaders decide what is good data. How do you determine that your data is unbiased? Here are some tips for how to identify the best possible data to inform your business decisions.

Be A Good Steward

Many business leaders invest in data governance, which designates staff to ensure quality and credibility of data. Whether it’s a chief data officer who leads an entire team, or a few dedicated employees, every organization needs at least one effective data steward to make sure data are being collected and handled properly.

Data collection conducted under stringent oversight enables business leaders to catch errors before that information is used to substantiate important decisions.

Data stewards can guard against misreading of data by emphasizing the importance of metadata—background information that defines the characteristics of data elements, said Manav Misra, chief science officer at CenturyLink Cognilytics.

“It’s not just a one-time thing,” he added. “Every time specific data is moved or touched, you have to ensure that the quality of the data is not compromised.”

Scrutinize Data Vendors

It’s one thing for an organization to rely on data that it has collected. But what about data generated by outside sources? Misra recommends applying the same level of scrutiny and control to both internal and external sources.

Ask third-party vendors if they can certify the data they’re providing, he advised. Does the vendor have a data governance program in place? What about logs that record how data is modified? Can the vendor see how the data originated, what path it took to get to your organization, and who touched it along the way?

Don’t be afraid to hold the bar as high for outside vendors as you do for your own business, he said.

Ask The Right Questions

Whether a business leader relies on internal or external sources of data, it’s critical to ask the right questions about how the information was collected, Misra said.

There are many biases that can impact data sets. Confirmation bias occurs when the person who does the analysis selects data that support a certain hypothesis or belief, he said.

Selection bias can occur when data are skewed because of the way that it’s collected, such as when a particular group is either excluded or over-represented in a survey.

To better understand how data are collected, business leaders should ask for details about the data model, or the framework used to organize the data. When a statistical model is too complex, it does a poor job of “generalizing,” or predicting future outcomes, Misra added.

Watch For Red Flags

Even when business leaders do everything they can to ensure that their data are accurate and unbiased, errors can still occur. That’s why it’s important to watch for red flags, including fluctuations in new data that is collected, Misra advised.

If data results vary widely from one report to the next, it could indicate that something is wrong in either the underlying data or in the analysis that was performed, he said.

Business leaders should also lean on their business knowledge to ask questions, Misra added. That’s what one credit card company business leader did when an analyst presented him with results that seemed to imply that the average customer had about 100 checking accounts. The business leader immediately disputed the conclusion and decided that the rest of the data was suspect as well.

Don’t be shy about investigating the quality of the data presented, or the analysis that was done, when the information defies experience and common sense, Misra said.

If businesses take the time to become trained in the basic understanding of what constitutes good data, they will reap the many benefits it will produce. To be a data driven business you must ensure the data you are receiving is the good data you can transform into solutions.

To read the full article click here.

Streamlined Reports Save Time and Money – Save With Data

Reducing Reporting Redundancies, Targeting and Consolidation Save 250 Hours per Month

Broadband operators rely on the billing systems to provide them with accurate and timely reports. These reports are crucial to day-to-day operations, and are increasingly important for both strategic planning and the execution of business goals.

The Client:

A region in one of America’s biggest Multi System Operators responsible for 250,000 customers.

The Business Issue:

The client was using time intensive manual processes to develop reports required to support ongoing business. Various queries were used to retrieve data from the billing systems, often times delivering inconsistent results across differing reports. The standard practice was to run and merge multiple reports from these queries, then copy the results from these queries and other sources into spreadsheets for further manipulation, all to arrive at questionable data. Coordination of reports between departments and groups was non-existent and there were no standardized Methods and Procedures (M&Ps) for developing these reports. The reporting group was stretched beyond capacity resulting in a multi-week backlog for the generation of new reports. The client retained Cliintel to help streamline and document the reporting process.

By identifying redundancies and targeting inefficient processes Cliintel achieved a reduction in internal resource workload by 250 hours/month, improved report accuracy, increased customer satisfaction and a return on the investment in less than 4 months.

The Approach:

Cliintel takes a holistic approach to every business issue presented for resolution. Our project professionals evaluate the situation, design a solution that fits, gain adoption and optimize performance. Cliintel’s focus was on increasing productivity and reducing costs by examining and evaluating reports for each department – independently and thoroughly. Using this holistic approach, Cliintel interviewed and met with end users in 8 departments to assess their reporting needs and to identify redundancy. Cliintel performed a gap analysis and inventoried and performed an audit of many of the crucial reports needed for day to day business operations. Labor intensive and inefficient manual processes were targeted and re-engineered. Consolidation opportunities were identified and redesigned.

The Solution:

Cliintel designed and developed new queries extracting only the relevant data necessary for each report. Database clean-up tools were created to ensure the accuracy of the data and work processes were streamlined to consolidate redundant reports.

Additionally Cliintel:

• Identified and re-designed current reports and procedures
• Implemented a strategic plan that included process re-engineering and system enhancements
• Implemented a KPI plan and provided ongoing KPI analysis framework
• Provided recommendations for further enhancements
• Provided a base for future reporting development

The Results:

By identifying redundancies and targeting inefficient processes Cliintel achieved a reduction in internal resource workload by 250 hours/month, improved report accuracy, increased customer satisfaction and a return on the investment in less than 4 months.

We’re proud to help our clients solve tough problems and achieve stunning results. To see what kind of results Cliintel can deliver for you, please visit www.cliintel.com or e-mail askcliintel@cliintel.com.

3D Printing, Sensors and Data Science Coming to America’s Factories

A new study from consulting giant McKinsey says factories are the next fertile ground for the “Internet of things.”

While the current trend toward automation and reduction of standard factory floor staff will continue, Kaas added that “any skill set which truly requires handling of data and analytics” will be highly in demand for manufacturers in the next decade.

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Bringing Life to Design: Data Science in 3D – Autodesk‘s Mike Haley

Want to solve your tough problems and achieve stunning results with big data?

Cliintel is a data analytics company that helps you make your business strategy happen. Their business systems and insights work hand in hand with your business strategies extracting the actionable intelligence that will help you accomplish your goals.

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Evaluation and Replacement of Subscriber Management System Enables Rapid Growth

For the burgeoning satellite radio industry, competition drove the market. Improving service delivery meant the difference between continued growth or a quick decline. To address this, the client, one of the world’s most aggressive providers of satellite radio, chose Cliintel to align its billing and operational support systems. The success of this project enabled the satellite radio provider to achieve their aggressive implementation objectives and advance their competitive position.

The Client:

The client, one of the largest satellite radio service providers offering a digital radio service that transmits 100 streams of digital-quality music and entertainment from its broadcast studios.

Through the utilization of Cliintel’s evaluation framework, the client was able to select the new billing system, configure, implement, convert the old data and scale the new system to 4x its original size in less than 7 months.

The Business Issue:

For this industry, the Subscriber Management System (SMS) holds the key to service delivery, facilitating gains in market share and ultimately shareholder returns. The client knew that their current SMS had functional shortcomings that were directly impacting its ability to collect revenue as well as provide the desired level of customer service. The system needed evaluation, and the client was prepared to implement a replacement if necessary. The client needed an aggressive assessment and plan that would work within operational and financial constraints.

The focus of the project was to evaluate the current billing system, define desired functionality and select a replacement subscriber management system if necessary.

The SMS needed to:

• Capture and make available timely, accurate, customer and partner data
• Enable testing and timely implementation of new services, marketing programs, and strategies to leverage continued growth
• Contribute to the growth and retention of customers, as well as partners, with cost-effective, reliable technologies.

A system strategy would also be developed to enable the client to grow the business aggressively, accommodate new business models, and respond to changing business conditions, without undue expense or over-reliance on manual processes and procedures.

Through the utilization of Cliintel’s evaluation framework, the client was able to select the new billing system, configure, implement, convert the old data and scale the new system to 4x its original size in less than 7 months.

The Solution:

Cliintel broke down the work structure into smaller, more manageable processes, which ensured the capture of all business-critical requirements. The methodology allowed for the collection, documentation and prioritization of system requirements without disrupting the ongoing operations of the client, or placing an undue burden on the team members who represented the functional areas. From this foundation, a requirements matrix was developed that enabled the client’s executive steering committee to highlight current critical business needs as well as identify areas requiring enhanced scalability.

Cliintel performed an SMS Gap Analysis on the current system, focusing on the best-practice constructs of features and functionality, adaptability, vendor risk, architecture and scalability, deployability and economics. As a result of this analysis the steering committee concluded that their current SMS provider lacked the stability and scalability the client would require. Cliintel was further engaged to help find a more suitable replacement solution.

This process involved an initial request for information (RFI) from 45 SMS service providers. Cliintel tailored the information requests to the clients business and system requirements and the same six constructs that were used to evaluate the legacy system. Analysis of the responses to the RFI led to the identification of the top five SMS providers, which were sent requests for proposals (RFPs).

Cliintel reviewed the RFPs and created executive “wrappers” for the steering committee, highlighting their evaluations. Invitations for product demonstrations were sent to the best candidates from this group. Cliintel created scenarios to test the extent of product functionality provided by the vendors. Cliintel also created scorecards to assist the client in ranking vendor performance during the demos.

Based upon the demonstrations, requested documentation, and interviews with the potential provider’s customers, Cliintel worked with the client in the selection of their new SMS provider. In addition, Cliintel created a high-level implementation plan and cost-estimates for the implementation, integration and management of the recommended solution.

The Project Results

Cliintel’s SMS evaluation allowed the client to select a system more suitable for their expanding business needs while keeping their focus on their core business. By prioritizing the requirements, the gap analysis enabled functional departments to more fully understand their business and establish a common goal. Through the utilization of Cliintel’s evaluation framework, the client was able to select the new billing system, configure, implement, convert the old data and scale the new system to 4x its original size in less than 7 months.

We’re proud to help our clients solve tough problems and achieve stunning results. To see what kind of results Cliintel can deliver for you, please visit www.cliintel.com or e-mail askcliintel@cliintel.com.

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Stretching Myths, Types of Stretching and Stretching Exercises

Stretching, in its most basic form, is a natural and an instinctive activity. Stretching results in a feeling of increased muscle control, flexibility, and range of motion.

There are four different types of stretching: ballistic, dynamic, proprioceptive neuromuscular facilitation, and static stretching.

By performing slow controlled stretches through full range of motion, a person reduces risk of injury.

5 Stretching Myths

To stretch or not to stretch? That is the age old question. Here’s everything you need to forget about stretching, and everything you should know instead.

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Stretching: What To Do and When To Do It

There are several variations of stretches to improve your range of motion. Why? Better range of motion means improved strength, which leads to speed and power.

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Stretching Exercises

Stretching exercises and workouts. Learn from experts using this stretching exercise database filled with detailed instructions and video.

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