Tag Archives: Data Management

Emerging Markets: Does Location Matter?

I recently wrapped up two overseas trips; one to Central America and another to South Africa. As such, I had the opportunity to meet with a national bank and a regional retail. It prompted me to ask the question: Does location matter in emerging markets?

I wish I could tell you that there was a common theme on how firms in the same sector or country (even city) treat data on a philosophical or operational level but I cannot.   It is such a unique experience every time as factors like ownership history, regulatory scrutiny, available/affordable skill set and past as well as current financial success create a unique grey pattern rather than a comfortable black and white separation. This is even more obvious when I mix in recent meetings I had with North American organizations in the same sectors.

Banking in Latin vs North America

While a national bank in Latin America may seem lethargic, unimaginative and unpolished at first, you can feel the excitement when they can conceive, touch and play with the potential of new paradigms, like becoming data-driven.  Decades of public ownership did not seem to have stifled their willingness to learn and improve. On the other side, there is a stock market-listed, regional US bank and half the organization appears to believe in meddling along without expert IT knowledge, which reduced adoption and financial success in past projects.  Back office leadership also firmly believes in “relationship management” over data-driven “value management”.

To quote a leader in their finance department, “we don’t believe that knowing a few more characteristics about a client creates more profit….the account rep already knows everything about them and what they have and need”.  Then he said, “Not sure why the other departments told you there are issues.  We have all this information but it may not be rolled out to them yet or they have no license to view it to date.”  This reminded me of the “All Quiet on the Western Front” mentality.  If it is all good over here, why are most people saying it is not?  Granted; one more attribute may not tip the scale to higher profits but a few more and their historical interrelationship typically does.

tapping emerging market

“All Quiet on the Western Front” mentality?

As an example; think about the correlation of average account balance fluctuations, property sale, bill pay account payee set ups, credit card late charges and call center interactions over the course of a year.

The Latin American bankers just said, “We have no idea what we know and don’t know…but we know that even long standing relationships with corporate clients are lacking upsell execution”.  In this case, upsell potential centered on wire transfer SWIFT message transformation to their local standard they report of and back.  Understanding the SWIFT message parameters in full creates an opportunity to approach originating entities and cutting out the middleman bank.

Retailing in Africa vs Europe

The African retailer’s IT architects indicated that customer information is centralized and complete and that integration is not an issue as they have done it forever.   Also, consumer householding information is not a viable concept due to different regional interpretations, vendor information is brand specific and therefore not centrally managed and event based actions are easily handled in BizTalk.  Home delivery and pickup is in its infancy.

The only apparent improvement area is product information enrichment for an omnichannel strategy. This would involve enhancing attribution for merchandise demand planning, inventory and logistics management and marketing.  Attributes could include not only full and standardized capture of style, packaging, shipping instructions, logical groupings, WIP vs finished goods identifiers, units of measure, images and lead times but also regional cultural and climate implications.

However, data-driven retailers are increasingly becoming service and logistics companies to improve wallet share, even in emerging markets.  Look at the successful Russian eTailer Ozon, which is handling 3rd party merchandise for shipping and cash management via a combination of agency-style mom & pop shops and online capabilities.  Having good products at the lowest price alone is not cutting it anymore and it has not for a while.  Only luxury chains may be able to avoid this realization for now. Store size and location come at a premium these days. Hypermarkets are ill-equipped to deal with high-profit specialty items.  Commercial real estate vacancies on British high streets are at a high (Economist, July 13, 2014) and footfall is at a seven-year low.   The Centre for Retail Research predicts that 20% of store locations will close over the next five years.

If specialized, high-end products are the most profitable, I can (test) sell most of them online or at least through fewer, smaller stores saving on carrying cost.   If my customers can then pick them up and return them however they want (store, home) and I can reduce returns from normally 30% (per the Economist) to fewer than 10% by educating and servicing them as unbureaucratically as possible, I just won the semifinals.  If I can then personalize recommendations based on my customers’ preferences, life style events, relationships, real-time location and reward them in a meaningful way, I just won the cup.

AT Kearney "Seizing Africa's Retail Opportunities" (2014)

AT Kearney “Seizing Africa’s Retail Opportunities” (2014)

Emerging markets may seem a few years behind but companies like Amazon or Ozon have shown that first movers enjoy tremendous long-term advantages.

So what does this mean for IT?  Putting your apps into the cloud (maybe even outside your country) may seem like an easy fix.  However, it may not only create performance and legal issues but also unexpected cost to support decent SLA terms.  Does your data support transactions for higher profits today to absorb this additional cost of going into the cloud?  Focus on transactional applications and their management obfuscates the need for a strong backbone for data management, just like the one you built for your messaging and workflows ten years ago.  Then you can tether all the fancy apps to it you want.

Have any emerging markets’ war stories or trends to share?  I would love to hear them.  Stay tuned for future editions of this series.

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Posted in Financial Services, Retail, Vertical | Tagged , , | Leave a comment

To Engage Business, Focus on Information Management rather than Data Management

Focus on Information Management

Focus on Information Management

IT professionals have been pushing an Enterprise Data Management agenda for decades rather than Information Management and are frustrated with the lack of business engagement. So what exactly is the difference between Data Management and Information Management and why does it matter? (more…)

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Posted in Architects, Business Impact / Benefits, Business/IT Collaboration, CIO, Data Governance, Data Integration, Enterprise Data Management, Integration Competency Centers, Master Data Management | Tagged , , , , | Leave a comment

Banking and Insurance Sessions at Informatica World 2014

Informatica World 2014Financial services is one of the most data-centric industries in the world.  Clean, connected, and secure data is critical to satisfy regulatory requirements, improve customer experience, grow revenue, avoid fines, and ultimately change the world of banking and insurance. Data management improvements have been made and several of the leading companies are empowered by Informatica.

Who are these companies and what are they doing with Informatica?

To find out more, register and attend Informatica World 2014, May 12-15 at the Cosmopolitan Hotel, in Las Vegas.

Fifteen of the top financial services companies will share their stories and success leveraging Informatica for their most critical business needs. These include:

Informatica World 2014 will have over 100 breakout sessions covering a wide range of topics for Line of Business Executives, IT decision makers, Architects, Developers, and Data Administrators. Our great keynote line up includes Informatica executives Sohaib Abbasi (Chief Executive Officer), Ivan Chong (Chief Strategy Officer), Marge Breya (Chief Marketing Officer) and Anil Chakravarthy (Chief Product Officer). Our series of speakers will share Informatica’s vision for this new data-centric world and explain innovations that will propel the concept of a data platform to an entirely new level.

Register today so you don’t miss out.

We look forward to seeing you in May!

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Posted in Business Impact / Benefits, Financial Services, Informatica World 2014 | Tagged , , , | Leave a comment

Fire your Data Scientists – They Don’t Add Value

Data ScientistYears ago, I was on a project to improve production and product quality through data analysis. During the project, I heard one man say: 

“If I had my way, I’d fire the statisticians – all of them – they don’t add value”. 

Surely not? Why would you fire the very people who were employed to make sense of the vast volumes of manufacturing data and guide future production?  But he was right. The problem was at that time data management was so poor that data was simply not available for the statisticians to analyze.

So, perhaps this title should be re-written to be: 

Fire your Data Scientists – They Aren’t Able to Add Value.

Although this statement is a bit extreme, the same situation may still exist. Data scientists frequently share frustrations such as:

  • “I’m told our data is 60% accurate, which means I can’t trust any of it.”
  • “We achieved our goal of an answer within a week by working 24 hours a day.”
  • “Each quarter we manually prepare 300 slides to anticipate all questions the CFO may ask.”
  • “Fred manually audits 10% of the invoices.  When he is on holiday, we just don’t do the audit.”

This is why I think the original quote is so insightful.  Value from data is not automatically delivered by hiring a statistician, analyst or data scientist. Even with the latest data mining technology, one person cannot positively influence a business without the proper data to support them.

Most organizations are unfamiliar with the structure required to deliver value from their data. New storage technologies will be introduced and a variety of analytics tools will be tried and tested. This change is crucial for to success. In order for statisticians to add value to a company, they must have access to high quality data that is easily sourced and integrated. That data must be available through the latest analytics technology. This new ecosystem should provide insights that can play a role in future production. Staff will need to be trained, as this new data will be incorporated into daily decision making.

With a rich 20-year history, Informatica understands data ecosystems. Employees become wasted investments when they do not have access to the trusted data they need in order to deliver their true value.

Who wants to spend their time recreating data sets to find a nugget of value only to discover it can’t be implemented?

Build a analytical ecosystem with a balanced focus on all aspects of data management. This will mean that value delivery is limited only by the imagination of your employees. Rather than questioning the value of an analytics team, you will attract some of the best and the brightest. Then, you will finally be able to deliver on the promised value of your data.

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Posted in Big Data, Business Impact / Benefits, Data Integration, Data Integration Platform, Data Warehousing | Tagged , , , | 4 Comments

How Can A Single View of Your Customer Helps You Better Manage your Customer Service? (Part One)

One thing we all have in common in this modern world, is that we have all, at some point in our lives, been on the receiving end of poor customer service.

Don’t get me wrong, a career in customer service is not an easy one, and I’m sure there are many service providers out there who have been wrongly on the receiving end of an angry customer, for reasons out of the businesses hands, that’s another topic in itself. It is hard, however, to ignore that one thing companies often fail on heavily is providing a timely, easy to access and appropriate level of service for their customers. (more…)

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Posted in Cloud Computing, Master Data Management | Tagged , , , , , , , | Leave a comment

Me, Myself and Midata

Some interesting news hit UK headlines last year that companies could be made to give the public greater access to their personal transaction data in an electronic, portable and machine-readable format. That’s if the midata project has anything to do with it.

Launched in April 2011 midata is part of the UK Government’s consumer empowerment strategy, Better Choices: Better Deals. Essentially, it’s a partnership between government, consumer groups and major businesses. Its aim is to give consumers access to the data that they produce, from the likes of household utilities, and banking, to internet transactions and high street loyalty cards. (more…)

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Posted in Application ILM | Tagged , , | Leave a comment

Data Scientist Named ‘Sexiest Job of the 21st Century’: Who is This Person?

Thomas Davenport, visiting professor at Harvard University and author of the watershed book Competing on Analytics, is once again making waves across the datasphere with his proclamation of data scientist as the “sexiest job of the 21st century.”

To many readers here at the Perspectives site, of course, this is not news, as many data professionals have increasingly been recognizing – and are being recognized – for the increasing power of information in driving new insights and business opportunities. (more…)

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Posted in Big Data, Data Integration | Tagged , , , , , , | 3 Comments

Reasons Why Cloud MDM Makes Perfect Sense

There are those who look at the emerging world of cloud computing as a trend to efficiency. It gives them the ability to leverage resources using much more cost effective models, where those resources are provisioned and shared amongst many consumers.

However, in the quest for efficiency, we often overlook the functionality of “the cloud.” Or, the usefulness of placing core features in a centralized location, which thus provides better control and governance, as well as efficiency. This leads to the placement of core enterprise data management services in the cloud, such as Master Data Management, or MDM.  (more…)

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Posted in Cloud Computing, Master Data Management | Tagged , , , , | 4 Comments

When It Comes to Data Quality Delivery, the Soft Stuff is the Hard Stuff (Part 3 of 6)

In my previous post I discussed effective stakeholder management and communications as a key enabler of successful data quality delivery. In this blog, I will discuss the importance of demonstrated project management fundamentals.

Large-scale, complex enterprise Data Quality and Data Management efforts are characterized by numerous activities and tasks being performed iteratively by multiple resources, across multiple work streams, with high volume units of work (i.e. dozens of source systems and data objects, hundreds of tables, thousands of data elements, hundreds of thousands of data defects and millions of records). Without the means to effectively define, plan and manage these efforts, success is nearly impossible. (more…)

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Posted in Big Data, Business Impact / Benefits, Data Governance, Data Quality, Enterprise Data Management, Master Data Management, Professional Services | Tagged , , | Leave a comment

Data Governance and Systemic Data Management Issues

We have been looking at how data management issues can be classified, and in my last post I provided five categories, but broken them down into two groups: Systemic and System. The systemic issues are ones in which process or management gaps allow data flaws to be introduced. A good example occurs when consumers of reports from the data warehouse insist that the data sets are incomplete, and the root cause is that the processes in which the data is initially collected or created do not comply with the downstream requirement for capturing the missing values. (more…)

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Posted in Data Governance, Data Quality | Tagged , , , , | Leave a comment