Business Linkage Analysis: An Overview

Customer feedback professionals are asked to demonstrate the value of their customer feedback programs. They are asked: Does the customer feedback program measure attitudes that are related to real customer behavior? How do we set operational goals to ensure we maximize customer satisfaction? Are the customer feedback metrics predictive of our future financial performance and business growth? Do customers who report higher loyalty spend more than customers who report lower levels of loyalty? To answer these questions, companies look to a process called business linkage analysis.

Figure 1. Companies who adopt linkage analysis get the insight that drives customer loyalty

Business Linkage Analysis is the process of combining different sources of data (e.g., customer, employee, partner, financial, and operational) to uncover important relationships among important variables (e.g., call handle time and customer satisfaction). For our context, linkage analysis will refer to the linking of other data sources to customer feedback metrics (e.g., customer satisfaction, customer loyalty).

Business Case for Linkage Analyses
Based on a recent study on customer feedback programs best practices (Hayes, 2009), I found that companies who regularly conduct operational linkages analyses with their customer feedback data had higher customer loyalty (72nd percentile) compared to companies who do conduct linkage analyses (50th percentile). Furthermore, customer feedback executives were substantially more satisfied with their customer feedback program in helping them manage customer relationships when linkage analyses (e.g., operational, financial, constituency) were a part of the program (~90% satisfied) compared to their peers in companies who did not use linkage analyses (~55% satisfied). Figure 1 presents the effect size for VOC operational linkage analyses.

Linkage analyses appears to have a positive impact on customer loyalty by providing executives the insights they need to manage customer relationships. These insights give loyalty leaders an advantage over loyalty laggards. Loyalty leaders apply linkage analyses results in a variety of ways to build a more customer-centric company: Determine the ROI of different improvement effort, create customer-centric operational metrics (important to customers) and set employee training standards to ensure customer loyalty, to name a few. In upcoming posts, I will present specific examples of linkage analyses using customer feedback data.

Linkage Analysis: A Data Management and Analysis Problem

Figure 2. Linking Disparate Business Data Sources Leads to Insight

You can think of linkage analysis as a two-step process: 1 ) organizing two disparate data sources into one coherent dataset and 2) conducting analyses on that aggregated dataset. The primary hurdle in any linkage analysis is organizing the data in an appropriate way where the resulting linked dataset make logical sense for our analyses (appropriate unit of analysis). Therefore, data management and statistical skills are essential in conducting a linkage analysis study. More on that later.

Once the data are organized, the researcher is able to conduct nearly any kind of statistical analyses he/she want (e.g., Regression, ANOVA, Multivariate), as long as it makes sense given the types of variables (e.g., nominal, interval) you are using.

Figure 3. Common Types of Linkages among Disparate Data Sources

Types of Linkage Analyses

In business, linkage analyses are conducted using the following types of data (see Figure 2):

  1. Customer Feedback
  2. Financial
  3. Operational
  4. Employee
  5. Partner

Even though I discuss these data sources as if they are distinct, separate sources of data, it is important to note that some companies have some of these data sources housed in one dataset (e.g., call center system can house transaction details including operational metrics and customer satisfaction with that transaction). While this is an advantage, these companies still need to ensure their data are organized together in an appropriate way.

With these data sources, we can conduct three general types of linkage analyses:

  1. Financial: linking customer feedback to financial metrics
  2. Operational: linking customer feedback to operational metrics
  3. Constituency: linking customer feedback to employee and partner variables

Before we go further, I need to make an important distinction between two different types of customer feedback sources: 1) relationship-based and 2) transaction-based. In relationship-based feedback, customer ratings (data) reflect their overall experience with and loyalty towards the company. In transaction-based feedback, customer ratings (data) reflect their experience with a specific event or transaction. This distinction is necessary because different types of linkage analyses require different types of customer feedback data (See Figure 3). Relationship-based customer feedback is needed to conduct financial linkage analyses and transaction-based customer feedback is needed to conduct operational linkage analyses.

Statistical Analyses

The term “linkage analysis” is actually a misnomer. Linkage analysis is not really a type of analysis; it is used to denote that two different data sources have been “linked” together. In fact, several types of analyses can be employed after two data sources have been linked together. Three general types of analyses that I use in linkage analyses are:

  1. Factor analysis of the customer survey items: This analysis helps us create indices from the customer surveys. These indices will be used in the analyses. These indices, because they are made up of several survey questions, are more reliable than any single survey question. Therefore, if there is a real relationship between customer attitudes and financial performance, the chances of finding this relationship greatly improves when we use metrics rather than single items.
  2. Correlational analysis (e.g., Pearson correlations, regression analysis): This class of analyses helps us identify the linear relationship between customer satisfaction/loyalty metrics and other business metrics.
  3. Analysis of Variance (ANOVA): This type of analysis helps us identify the potentially non-linear relationships between the customer satisfaction/loyalty metrics and other business metrics. For example, it is possible that increases in customer satisfaction/loyalty will not translate into improved business metrics until customer satisfaction/loyalty reaches a critical level. When ANOVA is used, the independent variables in the model (x) will be the customer satisfaction/loyalty metrics and the dependent variables will be the financial business metrics (y).

Summary

Business linkage analysis is the process of combining different sources of data to uncover important insights about the causes and consequence of customer satisfaction and loyalty. For VOC programs, linkage analyses fall into three general types: financial, operational, and constituency. Each of these types of linkage analyses provide useful insight that can help senior executives better manage customer relationships and improve business growth. I will provide examples of each type of linkage analyses in following posts.

Download a free white paper titled, “Linkage Analysis in your Voice of the Customer Program.”

Source by bobehayes

Meet the startup that is obsessed with tracking every other startup in the world

startup

At their previous jobs at venture capital firms, Sequoia Capital and Accel Partners, respectively, Neha Singh and Abhishek Goyal often had to help identify prospective startups and make investment decisions.

But it wasn’t always easy.

Startups usually don’t disclose information about themselves, since they are privately held firms and are under no compulsion to share data publicly. So, Singh and Goyal had to constantly struggle to collate information from multiple sources.

Eventually, fed up with the lack of a single source for data, the Indian Institute of Technology graduates quit their jobs in 2013 to start an analytics firm, Tracxn!. Their ambition: To become the Gartner—the go-to firm for information technology research—of the startup ecosystem.

“It’s almost surprising,” Singh told Quartz in an email interview, “that despite billions of dollars invested in each of the sectors (be in foodtech or mobile commerce, or payments, etc), thousands of people employed in this ecosystem and many more aspiring to start something here, there is not a single source which tracks and provides insights into these private markets.”

Tracxn! started operations in May 2013, working from Lightspeed Venture Partners’ office in Menlo Park, California, with angel funding from founders of e-commerce companies like Flipkart and Delhivery. In 2014, the startup began its emerging markets operation with focus on India and China.

“After our first launch in April last year, we scaled the revenues quickly and turned profitable last September, (and) grew to a team of 40,” Singh said. Most of its analysts are based in Bengaluru.

Tracxn! follows a SaaS (software as a service) business model, charging subscribers between $20,000 and $90,000 per year. With a database ofover 7,000 Indian and 21,000 US startups, Singh and Goyal now count over 50 venture capital funds among their clients, which also include mergers and acquisitions specialists, product managers, founders and aspiring entrepreneurs.

While firms like Mattermark, Datafox and CB Insights provide similar services, Tracxn! allows investors to get an overview of a sector within the ecosystem before drilling down to individual companies.

“For many funds, we have become a primary source of their deal discovery,” said Singh. “We want to become the default research platform for anyone looking for information and trends on these private markets and companies.”

In April this year, Tracxn! received $3.5 million in funding from private equity firm, SAIF Partners, which it plans to use to ramp up its analyst strength to 150 by the end of the year.

“We keep getting inquiries from investors across various countries (like from Europe, parts of Southeast Asia, etc),” explained Singh. “But we cannot launch them because we don’t have analyst teams for it.”

But with money on its way, Tracxn! now wants to expand coverage into Malaysia, Indonesia, Singapore, Philippines, Vietnam and Europe to build its global database.

Originally posted at: http://qz.com/401931/meet-the-startup-that-is-obsessed-with-tracking-every-other-startup-in-the-world/

Originally Posted at: Meet the startup that is obsessed with tracking every other startup in the world

Dec 14, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data interpretation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 AI and machine learning will make everyone a musician – Wired.co.uk Under  Machine Learning

>>
 Barnes & Noble Inc (NYSE:BKS) Institutional Investor Sentiment Analysis – WeeklyHub Under  Sentiment Analysis

>>
 Reposition DCIM systems for virtualization, container management – TechTarget Under  Virtualization

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[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:What is random forest? Why is it good?
A: Random forest? (Intuition):
– Underlying principle: several weak learners combined provide a strong learner
– Builds several decision trees on bootstrapped training samples of data
– On each tree, each time a split is considered, a random sample of m predictors is chosen as split candidates, out of all p predictors
– Rule of thumb: at each split m=?p
– Predictions: at the majority rule

Why is it good?
– Very good performance (decorrelates the features)
– Can model non-linear class boundaries
– Generalization error for free: no cross-validation needed, gives an unbiased estimate of the generalization error as the trees is built
– Generates variable importance

Source

[ VIDEO OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

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[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

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[ FACT OF THE WEEK]

235 Terabytes of data has been collected by the U.S. Library of Congress in April 2011.

Sourced from: Analytics.CLUB #WEB Newsletter

The What and Where of Big Data: A Data Definition Framework

I recently read a good article on the difference between structured and unstructured data. The author defines structured data as data that can be easily organized. As a result these type of data are easily analyzable. Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner. Unstructured data are not easy to analyze. A primary goal of a data scientist is to extract structure from unstructured data. Natural language processing is a process of extracting something useful (e.g., sentiment, topics) from something that is essentially useless (e.g., text).

While I like these definitions she offers, she included an infographic that is confusing. It equates the structural nature of the data with the source of the data, suggesting that structured data are generated solely from internal/enterprise systems while unstructured data are generated solely from social media sources. I think it would be useful to separate the format (structure vs. unstructured) of the data from source (internal vs. external) of data.

Sources of Data: Internal and External

Generally speaking, business data can come from either internal sources or from external sources. Internal sources of data reflect those data that are under the control of the business. These data are housed in financial reporting system, operational systems, HR systems and CRM systems, to name a few. Business leaders have a large say in the quality of internal data; they are essentially a byproduct of the processes and systems the leaders use to run the business and generate/store the data.

External sources of data, on the other hand, are any data generated outside the walls of the business. These data sources include social media, online communities, open data sources and more. Due to the nature of source of data, external sources of data are under less control by the business than are internal sources of data. These data are collected by other companies, each using their unique systems and processes.

Data Definition Framework

Data Definition Framework
Figure 1. Data Definition Framework

This 2×2 data framework is a way to think about your business data (See Figure 1). This model distinguishes the format of data from the source of data. The 2 columns represent the format of the data, either structured or unstructured. The 2 rows represent the source of the data, either internal or external. Data can fall into one of the four quadrants.

Using this framework, we see that unstructured data can come from both internal sources (e.g., open-ended survey questions, call center transcripts) and external sources (e.g., Twitter comments, Pinterest images). Unstructured data is primarily human-generated. Human-generated data are those that are input by people.

Structured data also can come from both inside (e.g., survey ratings, Web logs, process control measures) and outside (e.g., GPS for tweets, Yelp ratings) the business. Structured data includes both human-generated and machine-generated data. Machine-generated data are those that are calculated/collected automatically and without human intervention (e.g., metadata).

The quality of any analysis is dependent on the quality of the data. You are more likely to uncover something useful in your analysis if your data are reliable and valid. When measuring customers’ attitudes, we can use customer ratings or customer comments as our data source. Customer satisfaction ratings, due to the nature of the data (structured / internal), might be more reliable and valid than customer sentiment metrics from social media content (unstructured / external); as a result, the use of structured data might lead to a better understanding of your data.

Data format is not the same as data source. I offer this data framework as a way for businesses to organize and understand their data assets. Identify strengths and gaps in your own data collection efforts. Organize your data to help you assess your Big Data analytic needs. Understanding the data you have is a good first step in knowing what you can do with it.

What kind of data do you have?

 

Source

Why You Must Not Have Any Doubts About Cloud Security

The fear of the unknown grips all when adopting anything new and it is therefore natural that there are more skeptics when it comes to Cloud computing, which is a new technology that not everybody understands. The lack of understanding creates fear that makes people worry without reason before they take the first step in adapting the latest technology.  The pattern has been evident during the introduction and launch of any new technology and the advent of Cloud is no exception. It is therefore not a surprise that when it comes to Cloud computing, the likely stakeholders comprising of IT professionals and business owners are wary about the technology and often suspicious about its security level.

Despite wide-scale adoption, more than 90% enterprises in the United States use the Cloud, and there are mixed feelings about Cloud security among companies.  Interestingly, it is not the enterprise alone that uses the Cloud services NJ because it attracts a large section of small and medium businesses too, with 52% SMBs utilizing the platform for storage. The numbers indicate that the users have been able to overcome the initial fear and now trying to figure out what the new technology is. There is a feeling that the Cloud security is inferior to the security offered by legacy systems and in this article, we will try to understand why the Cloud is so useful and why there should not be concerns about the security.

The perception of Cloud security

The debate rages around whether the Cloud is much more secure or somewhat more secure than legacy systems  It has been revealed in a survey that  34% IT professionals feel that the Cloud is slightly more secure but not as much secure that would give them the confidence to rank it a few notches above the legacy systems. The opinion stems from the fact that there have been some high profile data breaches in the Cloud at Apple iCloud, Home Depot, and Target but the breaches resulted not from shortcomings of the Cloud security but due to human factors. Misinformation and lack of knowledge are reasons for making people skeptical about Cloud security.

Strong physical barriers and close surveillance

There used to be a time when legacy systems security was not an issue because denying access to on-premise computers was good enough to thwart hackers and other intrusions. However, it can be difficult to implement proper security in legacy systems comprising of the workstation, terminal, and browser that make it unreliable.  Businesses are now combining legacy systems with the Cloud infrastructure together with the backup and recovery services thus making it more vulnerable to security threats from hackers. Moreover, it is not easy to assess the security of legacy systems that entail a multi-step process that tends to indicate that replacing the legacy system is a better option.

While a locked door is the only defense in most offices to protect the computer system, Cloud service providers have robust arrangements for physical security of data centers comprising of barbed wire, high fences, concrete barriers, security cameras and guards for patrolling the area. Besides preventing people from entering the data center, it also monitors activities in the adjoining spaces.

Access is controlled

The threat is not only from online attackers that try to breach the system, but the threat also comes from people gaining easy physical access to the system that could make it more vulnerable. Cloud service providers ensure complete data security through data encryption during storage, organizations are now turning to selective data storage by using the Cloud facility for storing sensitive data offsite and keep it inaccessible from unauthorized persons. It reduces the human risk of causing damage since only the authorized users get access to sensitive data that remains securely stored in the Cloud. No employee, vendors or third parties can access the data by breaching the security cordon.

Assured cybersecurity

Cloud service providers are well aware of the security concerns and adopt robust security measures to ensure that once data reaches the data centers, it remains wholly protected. The Cloud is under close monitoring and surveillance round the clock that gives users more confidence about data security. When using the Cloud services, you not only get access to the top class data center that offers flexibility and security but also you receive the support of qualified experts who help to make better use of the resource for your business.

Auditing security system

To ensure flawless security to its clients, Cloud service providers conduct frequent auditing of the security features to identify possible weaknesses and take measures to eradicate it. Although the yearly audit is the norm, the interim audit may also take place if the need arises.

As the number of Cloud service users keep increasing, it adequately quells the security fears.

Source

Dec 07, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Big Data Analytics Bottleneck Challenging Global Capital Markets Ecosystem, Says TABB Group by analyticsweekpick

>> Best & Worst Time for Cold Call by v1shal

>> The real-time machine for every business: Big data-driven market analytics by thomassujain

Wanna write? Click Here

[ NEWS BYTES]

>>
 More hospital closings in rural America add risk for pregnant women – Reuters Under  Health Analytics

>>
 Statistics show fatal, injury crashes up at Sturgis Rally compared to this time last year – KSFY Under  Statistics

>>
 The power of machine learning reaches data management – Network World Under  Machine Learning

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[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is random forest? Why is it good?
A: Random forest? (Intuition):
– Underlying principle: several weak learners combined provide a strong learner
– Builds several decision trees on bootstrapped training samples of data
– On each tree, each time a split is considered, a random sample of m predictors is chosen as split candidates, out of all p predictors
– Rule of thumb: at each split m=?p
– Predictions: at the majority rule

Why is it good?
– Very good performance (decorrelates the features)
– Can model non-linear class boundaries
– Generalization error for free: no cross-validation needed, gives an unbiased estimate of the generalization error as the trees is built
– Generates variable importance

Source

[ VIDEO OF THE WEEK]

RShiny Tutorial: Turning Big Data into Business Applications

 RShiny Tutorial: Turning Big Data into Business Applications

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

 @BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

In 2008, Google was processing 20,000 terabytes of data (20 petabytes) a day.

Sourced from: Analytics.CLUB #WEB Newsletter

21 Big Data Master Data Management Best Practices

21 Big Data Master Data Management Best Practices
21 Big Data Master Data Management Best Practices

Master Data Management (MDM) is the process of establishing and implementing standards, policies and tools for data that’s most important to an enterprise, including but not limited to information on customers, employees, products and suppliers.

Per Wiki:
In business master data management (MDM) comprises the processes, governance, policies, standards and tools that consistently defines and manages the critical data of an organization to provide a single point of reference.[1]
The data that is mastered may include:
master data – the business objects for transactions, and the dimensions for analysis
reference data – the set of permissible values to be used by other data fields
Transactional data – supports applications
Analytical data – supports decision making [2]
In computing, An MDM tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. Master data are the products, accounts and parties for which the business transactions are completed. The root cause problem stems from business unit and product line segmentation, in which the same customer will be serviced by different product lines, with redundant data being entered about the customer (aka party in the role of customer) and account in order to process the transaction. The redundancy of party and account data is compounded in the front to back office life cycle, where the authoritative single source for the party, account and product data is needed but is often once again redundantly entered or augmented.

So, with task such important Master Data must be designed appropriately and after careful consideration to variour bells and whistles which are responsible for success and failure of the project. Following are top 21 bestpractices that needs to be considered before applying a good data management strategy.

1. Define “What is the business problem we’re trying to solve?”:
With so much data and so many disperate data sources, it is very easy to get lost in translation. So, a mental road map on the overall objective will help in keeping the effort streamlined.

2. Understand how the project helps to prep you for big data:
Yes, growing data is a concern and it should be sorted out at the planning stage. It is important to identify how master data management strategy will prepare your organization not only for generic enterprise data but to cope up with ever increasing big data.

3. Devise a good IT strategy:
Good IT strategy always go hand in hand with a good data strategy. A disfucntional IT strategy could really throw off a most efficient designed data management strategy. A good IT strategy increase the chances of success for a good MDM strategy by several degrees.

4. Business “users” must take full ownership of the master data initiative:
It’s important that business and it’s users must take full ownership of the inititaitve. A well defined ownership will save project from several communication failure which is almost everytime responsible for any project failure.

5. Allow ample time for evaluation and planning:
A well laid out planning stage ensures all the cracks and crevices are sorted out before project is rolled out. A rushed project often increases the rist of failure. Don’t underestimate the time and expertise needed to develop foundational data models.

6. Understand your MDM hub’s data model and how it integrates with your internal source systems and external content providers:
When data model problems cropped up relatively late in the project, whether it was a disconnect between the hub and an important source system, or a misalignment between data modeled in the hub and an external information provider, it was very disruptive. These problems can be avoided by really understanding how the hub is designed, and then mapping that back to your source systems and your external information sources.

7. Identify the project’s mission and business values:
This is another important area that needs it’s due attention. A clear project mission and business value definition helps in making sure high ROI is thought for and planned after in the process. One must link the initiatives to actionable insights.

8. Choose the best technology platform:
Choosing a good technology is important as well. Remeber, you don’t change your technology daily, so putting some thought and research into it makes a lot of different in sustainability of the project. A good technology should help organization grow to next several years without presenting too much growth bottlenecks.

9. Be real and plan a multi-domain design:
In a real world, many MDM technologies grew up managing one particular type of master data. A good strategy must be consistent across. So, applying the same approach to the various master data domains, whether those be customer, product, asset, supplier, location or person is a good strategy.

10. Active, involved executive sponsorship:
Most organizations are very comfortable with their “islands of data” and with technology being implemented in silos. For someone in the organization to come along and suggest changing that status quo, and to start managing critical information centrally, treating it as a true corporate asset, is going to mean some serious cultural change.

11. Use a holistic approach – people, process, technology and information:
This may be the most important best practice. You’ve got to start with the people, the politics, the culture, and then to make sure you spend at least as much time on the business processes involved in data governance and data stewardship. These really deserve a separate article of their own.

12. Pay attention to organizational governance:
You must have a very strong governance model that addresses issues such as change management and knowledge transfer. Afterall, the culture in an organization is a most important entity and a sorted plan to derisk project from it ensures success.

13. Build your processes to be ongoing and repeatable, supporting continuous improvement:
Data governance is a long term proposition. As a reality of any enterprise life, as long as one is in business, enterprise will be creating, modifying, and using master data. So if everyone in the company relies on them, but no one is specifically accountable for maintaining and certifying their level of quality, it shouldn’t be a surprise that, over time, like everything else, they become more and more chaotic and unusable. So plan from the beginning for a “way of life”, not a project.

14. Have a big vision, but take small steps:
Consider the ultimate goal, but limit the scope of the initial deployment, users told Ventana. Once master data management is working in one place, extend it step by step, they advised. Business processes, rather than technology, are often the mitigating factor, they said, so it’s important to get end-user input early in the process.

15. Consider potential performance problems:
Performance is the 800-pound gorilla quietly lurking in the master data management discussion, Loshin cautioned. Different architectures can mean different performance penalties. So, make some room for repair.

16. Management needs to recognize the importance of a dedicated team of data stewards:
Just as books belong in a library and a library needs librarians, master data belongs in a dedicated repository of some type, and that repository needs to be managed by data stewards. It is cruicial to start with convincing management of the need for a small team of data stewards who are 100% dedicated to managing the enterprise’s master data.

17. Consider the transition plan:
Then, there’s the prospect of rolling out a program that has an impact on many critical processes and systems — no trivial concern. Loshin recommended that companies should plan an master data management transition strategy that allows for static and dynamic data synchronization.

18. Resist the urge to customize:
Now that commercial off-the-shelf hub platforms have matured a bit, it should be easier to resist the temptation to get under the hood and customize them. Most vendors are still revving their products as often as twice a year, so you definitely don’t want to get into a situation where you are “rev locked” to an older version.

19. Stay current with vendor-provided patches:
Given the frequency of point releases, patches and major upgrades, you should probably plan for at least one major upgrade during the initial implementation, and be sure to build “upgrade competency” in the team that will maintain the hub platform after the initial project goes live.

20. Carefully plan deployment:
With increasing MDM complexity, training of business and technical people is more important than ever. Using untrained or semi-trained systems integrators and outsourcing attempts caused major problems and project delays for master data management users.

21. Test, test, test and then test again:
This is like the old saying about what’s important in real estate – “location, location, location”. Your MDM hub environment is going to be different, by definition, than every other environment in the world.

Originally Posted at: 21 Big Data Master Data Management Best Practices

How Retailer Should Use QR Code To Hit The Pot Of Gold

Before we dive into the topic, I want to take a step back and explain what is QRCode: QR Code (abbreviated from Quick Response Code) is the trademark for a type of matrix barcode (or two-dimensional code) first designed for the automotive industry. More recently, the system has become popular outside the industry due to its fast readability and large storage capacity compared to standard UPC barcodes. The code consists of black modules (square dots) arranged in a square pattern on a white background. The information encoded can be made up of four standardized kinds (“modes”) of data (numeric, alphanumeric, byte/binary, Kanji), or through supported extensions, virtually any kind of data.(per wikipedia).
To me, QRCode is an amazing magic wand that has the power to connect analog world to the digital world. It has the power to engage a motivated customers who is scanning QR Code and convert them to loyalists. From the day I was introduced to QR Code to today, I am extremely excited for what QR Code is worth, but at the same time, severely impacted by how underutilized it is. For the sake of this blog, and to understand what stores near-by are doing with their QRCode, I visited my nearest mall and clicked photos of the first few QR executions. To my surprise, it did not take me much to find or click quick snapshots of few different type of QR implementations. But amazing thing is that they all are doing it wrong. I will get on it soon. QRCode is facing some challenges with adoption, but with capable mobile devices, it is bound to pick up if it has not already. With this slow QR Code adoption, the only thing retailers need is a lousy execution throwing away users from using this amazing digital wonder of the world.

So, what are retailers doing wrong?
QRCode deployed covers used cases ranging from “signup with our mailing list”, “download our app”, to “visit our social page”. There was no consistency in execution. Every store wants users to juggle in different ways. Below are 5 used cases that I came across. It is very likely that most of the retail store QRCode implementation falls into one of these. I can understand that retailers are still experimenting with QRCode projects and understanding the impact. But consider this: A user, who is motivated to click a QRCode, puts in considerable effort to do lots of clicks to get to other side. So, what is it all worth- A facebook like, a twitter follow, an app download or a signup for mailing list? Having a QRCode should be taken as similar to having a domain. Try having a domain name pointing to all these services. Just like domain names, QRCode are precious as well. It is a perfect way to engage an already committed user. So, why throw vague click-to-action at them. Why not grab their attention for something that is win-win for both the retailer and the customer.

Following are implementations of retail QRCode – “The Good, The Bad and The Ugly sides”.

1. I got this image from someone and found it very interesting to share. It has some pros and cons to it.

How retailer should use QR Code to hit the pot of gold
The Good: QR Code is sitting in the primary location, gate is the first interface and attaching QR Code made it easily accessible. So, kudos for that.
The Bad: Is “facebook like” or “ twitter follow” that important? If a user jumping through hoops to scan a QRCode, to like a facebook page is appreciative of their effort? Is it providing enough value to retailer or user?
The Ugly: See at the end.

 

 

2. This is another example from a smoothie joint near the mall area, closer to my place.

How retailer should use QR Code to hit the pot of goldThe Good: It is great to separate interest groups, people with different intent will pick appropriate click to action. Here Yelp and Facebook audience are provided with separate QRCode.
The Bad: Confusion. With limited adoption and involvement, it is way too risky to have 2 QRCodes. It also exposes the campaign to technical issues. What if user scans them from distance that both QR show up etc. This implementation raises more questions than answers.
The Ugly: See at the end.

 

 

3. This is taken from a nearby Van Heusen outlet store from nearby mall.

How retailer should use QR Code to hit the pot of goldThe Good: $5 is very appreciative for the effort user is going through. This is gratifying users for their effort. $5 worked magic when it comes to fixing the eyes to banner as well.
The Bad:  Confusing plate, 2 offers are bundled into one plate. It could confuse users. Text & QR are packaged into one.
The Ugly: See at the end.
 

4. This image is taken at a local GMC store. I like the way they explained the used case. I find no problem in understanding how to use it. But then, I am not sure if it’s usable for all audience.

How retailer should use QR Code to hit the pot of goldThe Good: Very well laid out plan on how to engage with the used case.
The Bad: Only caters to deal hunters; what if you are not here for deals?
The Ugly: See at the end.

 

 

 

 

5. This image was taken from a nearby Costco. I visit there often and never paid attention to this banner until recently.

How retailer should use QR Code to hit the pot of goldThe Good: The position of the banner. It was kept right above the checkout counter. In case a long queue is awaiting, user could use that waiting time to indulge with the banner.
The Bad: A descriptive banner, with fine prints and asking user to download an app. App is very intimate to users due to limited real-estate on mobile phone. It is asking the user too much of commitment while waiting.
The Ugly: See below.
The Ugly: Almost all the used cases suffer from the same issue, not creating a bi-directional engagement interface with user. QRCode is used when user is actually physically present in the store and scanning. Also, it is not known at the moment what the user could be suffering from, so, selling them something without knowing what they want to buy is not a great idea. So, it is important for retail stores to provide a dashboard that could better address their current need (tools to help a surfing user) and once current issue is addresses, provide them with an opportunity to convert those users by offering app download links, social follow buttons or email newsletter signups.

From the observation above, few things stand out. Retailer gets the importance of QRCode but still lack a used case that will help engage their customers better with their brand. As with all great brands, listening is as important of a task as talking. So, why QRCode should be any different? They should also have ability to listen to customer as well as talk to them. Therefore QRCode should be primarily thought of as a tool to engage an active customer.

It is important to look at QRCode from a different lens. Unlike, facebook like, mobile app, social follow, QRCode is used when user is actively engaged in a store, so selling them engagement tools for future might not be something that is uber targeted. However, one could obviously cross-sell those tools, on landing page when user scans a QRCode. So, QRCode interfaces should be handled differently and should not be mixed with loyalty tools.

So, an Ideal QRCode should have following components:

1. Single QRCode addressing all the needs of the user.
2. A well accessible placement of the QRCode, making it easily discoverable.
3. Well laid out procedure to help users engage with QRCode.
4. QRCode bringing users to a super dashboard that could help them in any possible way it can. i.e. providing product descriptions, deals, specials, live chats, app links etc.
5. Providing capability for users to leave comments, complaints, suggestion and fill surveys.
6. Ability to further help users extend the engagement by providing links to social media channels, apps, email list, newsletters etc.
7. Providing access to email list.

Based on the business, users, and used case, there may be more of less used cases as stated above, but the overall coverage should be pretty much same.

So, an advice to all the retailers, get back to whiteboards and rethink existing QRCode strategy. It is a big pot of gold if done right. As holiday season is approaching, this could be a great opportunity to connect with masses and engage with them by designing a perfect system.

Let us know if we could help.

and just to confuse the hell out of you.. below is a video that states 37 places to use QR, and yes, most of them are just bad places to use QRCode. My good friend Steve posted a blog on 9 Ways To Screw Up QR Codes at Trade Shows [Infographic], make sure to check it out.

Originally Posted at: How Retailer Should Use QR Code To Hit The Pot Of Gold

Nov 30, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

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Q:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
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column names are values, not names, e.g. 26-45…
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* Removing observations

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The convoluted world of data scientist

The convoluted world of data scientist.
The convoluted world of data scientist.

Data scientists are not dime a dozen and they are not in abundance as well. Buzz around bigdata has produced a job category that is not only confusing but has been costing companies a lot in their stride to look through the talent pool to dig for a so called data scientist. So, what exactly is the problem and why are we suddenly seeing a lot of data scientist emerging from nowhere with very different skill sets? To understand this we need to understand the bigdata phenomena.

With emergence of big data user companies like Google, Facebook, yahoo etc. and their amazing contribution to open source, new platforms have been developed to process too much data using commodity hardware in fast and yet, cost efficient ways. Now with that phenomenon, every company wants to get savvier when it comes to managing data to gain insights and ultimately building competitive edge over their competitors. But companies are used to understanding small pieces of data using their business analysts. But talk about more data and more tools. Who will fit in? So, they started on lookout for special breed of professional that have the capability to deal with big data and it’s hidden insights.

So, where is the problem here? The problem lies in the fact that only one job title emerged from this phenomenon- data scientist. The professionals who are currently practicing some data science via business analysis, data warehousing or data designing jumped on the bandwagon grabbing the title of the data scientist. What is interesting here is that data scientist job as explained above does not deserve a single job description so it should be handled accordingly. It was never a magical job title that has all the answers for any data curious organization, to be able to understand, develop and manage a data project.

Before we go into what companies should do, let’s reiterate what is a data scientist. As the name suggest, it is something to do with data and scientist. Which means it should include job description that has done some data engineering, data automation, and scientific computing with a hint of business capabilities. If we extrapolate, we are looking at a professional with computer science degree, doctorate in statistical computing and MBA in business. What would be luck in finding that candidate and by-the-way, they should have some industry domain expertise as well. What is the likelihood that such a talent exists? Rare. But, even if they are in abundance, companies should tackle this problem at much granular and sustainable scale. And one more thing to note here is that no two data scientist job requirements are the same. This means that your data scientist requirement could be extremely different from what anyone else is looking for in a data scientist. So, why should we have one title to cater to such a diverse category?

So, what should companies do? First it is important to understand that companies are building data scientists’ capabilities and should not be hiring the herd of data scientists. This means that companies/ hiring managers should understand that they are not looking for a particular individual but a team as a solution. It is important for businesses to clearly articulate those magic skillsets that their so-called data scientist should carry. Following this drill, companies should split the skillset into categories, Data analytics, Business analyst, data warehousing professionals, software developer, and data engineers to name a few. Finding a common island where business analysts, statistical computing modelers and data engineers work in harmony to address a system that handles big data is a great start. Think of it as putting together a central data office. Huh! another buzz word. Don’t worry; I will go into more details in the follow-up blogs. Think of it as a department where business, engineering and statistician work together on a common objective. Data science is nothing but an art to find value in lots of data. So, big-data is to build capability to parse/analyze lots of data. So, business should work through their laundry list of skillset. First identify internal resources that could accommodate that list. Following this, companies should form a hard matrix structure to prove the idea of set of people working together as a data scientist. BTW I am not saying that you need one individual from each category, but, together the team should have all the skills mentioned above

One important take away for companies is to understand that the moment they came across a so called data scientist, it is important to understand which side of data scientist the talent represents. Placing that talent in their respective silo will help provide a clearer vision when it comes to understanding the talent and understanding the void that could stay intact if the resources are not filled accordingly. So, living in this convoluted world of data scientist is hard and tricky. Having some chops into understanding data science as a talent, companies could really play the big data talent game to their advantage and lure some cutting edge people and grow sustainably.

Originally Posted at: The convoluted world of data scientist by v1shal