Big Data – plenty of it,… but still missing

Big Data is a big is a Big buzzword. Although it brings huge opportunities, the “hype talks” might miss lead you to wrong decision that Big Data is cure for all. The truth is pretty simple – Big Data can give those answers that are hidden within the analyzed data set.

Spring 2010, at a small Croatian town there is an unusual meeting going on in a factory that we will call PPP. The meeting room at the first floor of the administrative building, just next to the gray production halls, hosted a group of about 10 different people. Individuals in the room take part of a fiery debate about the presentation that’s been projected on the wall. The discussion of a group of people in white coats, most probably production and development engineers, is obviously driven by the two most active members, often arguing with conflicting views among each other. There is a few people dressed in suites. One would guess that those are consultants and the company’s management team. Part of that group is quiet; they are just listening, and nodding from time to time to show their mental presence in the debate. Two persons from the group in suites ask a lot of questions. Some other participants are very active as well. They draw on the board and answer the questions with lively gestures. Those are mostly members of the academic community that take part of one of the EU “cross-border cooperation” projects, which is actually the reason of this colorful meeting.

Data Explosion1
Digitak explosion visual art

Let’s add sensors
In order to improve the efficiency of the production process and product quality, PPP initiated enrichment of certain phases of production by additional sensors, PLC and SCADA elements. By increasing the number of sensors from 12 to 35 per production machine, PPP started one of numerous initiatives around the world that contribute to the enormous global growth of machine generated data, the one we like to call Big Data. At one point, a temperamental professor with a French beard took stage. He passionately explains to the group recent results gathered from mining of the newly established data sets based on the increased number of sensors. No matter how colorful graphs were clear and despite the insight that was much above the previous findings, it was hard not to recognize the indifference on the faces of other participants in the meeting. Something is missing!

Data model or a Swiss cheese?
The whole initiative should provide, if not revolutionary, then at least usable insights. “We need to close the circle!”. All of a sudden, eyes of the participants were turned on the consultant who had been silent so far. “We need to close the information circle. You have all the parameters of the machine, but you really should start from the goals. You have to ensure traceability and link quality of the products with different stages of the production process and their parameters. Otherwise the new parameters won’t have much to say.” It is difficult to add IT tags to the hot metal castings that are being produced by the machines at PPP, so the data that was supposed to link the quality achieved and the level of waste with the 35 newly established parameters was simply missing.

Big Data: new methods, old constrains
Concluding superficially, Big Data might be perceived as a cure for everything: “now that we have so much information available, it is enough to develop mathematical algorithms and we will find all the answers.” But the truth is exactly the opposite. Today we have plenty of mathematical algorithms – from those that recognize your face, the tone of your voice or your fingerprint to those which understand the context of human speech, but the ways in which we traditionally collect data (processes) are not aligned with the technological capabilities of finding data patterns and filtering it through massive parallel processing (technology). More specifically, Big Data technologies will surely find patterns through a large amount of data, but those will not always propose answers to your problem or give you new relevant insights. In the same way, the data mining in PPP provided insight into the machine behavior such as stability patterns of certain parameters during the production cycles, including some insightful deviations. But it offered no answers about how those deviations and patterns affected the only thing that really mattered – the quality of the product. The answers must be included somewhere within the data set that we explore. They have to take part of the meta model of the entity that we analyze, or we must be able to deduct it from attributes of other entities that are similar enough to the one we study (i.e. the data on the quality of the product of hundreds of similar or identical machines worldwide, in case of PPP).

You can read more in the May 2013 issue of the Mreža magazine (Croatian language only), or later during the year translated to English at Alen’s Thing Place.

This work is Copyright of Alen Gojceta. You are not allowed to use the article, or any of its part in commercial or academic work without citing the author and this link.

Brief History of CRM

The post presents the history of Customer Relationship Management (CRM) within the context of academic research and business applications. This is the excerpt English from the article published in Croatian in April 2010 in “Mreza”, magazine for IT professionals. The CRM history is described from its starts in mid eighties to day, with a view on the years to come.

The history of CRM can’t be observed without considering the development of business applications with contemporary academic research.

Eighties, THE second half

MARKETING SCHOLARS: After the concept of services marketing, particularly developed within the so-called Nordic school of marketing, a new concept has emerged. Early definitions of Relationship Marketing could be found mid eighties (1985 Jackson).

BUSINESS APPLICATIONS: Sales Force Automation (SFA) and Customer Service (CS) applications were still considered as part of the wide family of ERP solutions. Few years later a new and distinct software solution category emerged and SFA and CS became part of so called Customer Relationship Management (CRM) software.

Nineties, the first half

MARKETING SCHOLARS: Relationship Marketing has been studied by Morgan and Hunt (1994) and Reichheld (1996). In 1995 Relationship Marketing was defined by Koiranen as “approach to establishing, keeping and enhancing the long-term relationships with customers and other shareholders.”

BUSINESS APPLICATIONS: Analysts from the first half of the nineties still did not recognize the rising strength of CRM. SFA and CS were classified as a small sub-segment of the ERP market. In 1994 the total CRM software (SFA and CS) market amounted to around 200 million US dollars, compared to the 6.4 billion of the global ERP sales.

Nineties, the second half

MARKETING SCHOLARS: Unlike Relationship Marketing, the CRM was studied relatively late by the academics. First academic definitions of CRM were written relatively late compared to Relationship Marketing. In the 1999 Srivastava, Shervani and Fahey described CRM as a broader concept than Relationship Marketing defining it as “a process that identifies customers, creates knowledge about customers, builds relationships with customers, and forms customers’ perception around the organization and its offerings.”

BUSINESS APPLICATIONS: Towards the end of the nineties, CRM fever heats up. The awareness of the big new market is rapidly growing. Everyone sees the opportunity for a continued growth in a somewhat saturated ERP market. During previous years, ERP applications have generated and stored an impressive amount of data about customers, so indispensible to fuel successful CRM initiatives.

Year 2000 to date

MARKETING SCHOLARS: Despite some terminological dissonance, academics are better aligned with the business practice. Through basic research, they contribute to the development of business concepts that are being embedded within CRM.

BUSINESS APPLICATIONS: Many applications are experiencing their maturity, backed up by better understanding of business processes as well as motivation agendas of individuals and departments within organizations . This maturity was influenced by the evolution of information technologies backed up by cheaper and faster data storage systems, accessible broadband and flexible IT environments, like service-oriented architecture (SOA), SAAS (software as a service) and cloud computing.

2010 and years to come

MARKETING SCHOLARS: The intensive progress in behavioral studies over the past two decades creates the basis for “intelligent” approach to large customer segments. In addition to corporate business systems, such segments are emerging within the social networks of the coming years.

BUSINESS APPLICATIONS: Social networks are opening a new page on the CRM agenda. Facebook, Twitter, LinkedIn, Second Life and other engaging social media networks, enable a more precise segmentation, affinity grouping, customer participation in offer definition and their impact on formulating corporate strategies, in a way that has never been possible before.

 

Social Networks' role in CRM
Social networks in CRM ecosystems enable a more precise segmentation and customer involvement. ©2010 Alen Gojčeta, Mreza magazine April 2010

This is the excerpt in English from the article published in the issue of April 2010 of Croatian magazine for IT professionals “Mreža“. The work above us the copyright of Alen Gojčeta and the Mreža magazine. The CRM history is part of the findings of the research done for author’s master degree thesis “Segmentation models in CRM”, 2006.

 

Story about Mr. S and the failed CRM project

Small insight in my latest article published in Mreza magazine (www.bug.hr/mreza). This is a story inspired by a true event and used as an descriptive introduction to the topic of CRM implementation. The complete article is available in Croatian in the April 2010 issue of Mreza magazine (www.bug.hr/mreza).

The story below is inspired by a true business case. I used it as introduction to the topic of CRM implementation in the latest issue (April 2010) of Mreza magazine (www.bug.hr/mreza). Herein translated to English.

At the end of winter 2003, Mr. S, member of the board of a large telecom operator, walked nervously from end to end of his office on the top of a glossy office building. In front of him there where two little men sitting on a leather sofa. One of them, an external consultant, was staring at the floor. The second one, the project manager from the company, was speaking with trembling voice, trying to explain the circumstances that led to the collapse of one of the largest CRM implementations in the world that year. The situation was more difficult because the bad news came too late. Mr. S has reserved, only a week ago, part of a fancy Austrian ski resort to celebrate success with project teams of a “big bang” implementation of almost all modules of the world’s leading CRM application.

Mr. S believed that the implementation of CRM applications will link his company by steady, predefined, user-oriented processes that will remove communication barriers with customers, reduce human mistakes and, ultimately, fend off new operators who where sharpening their balance sheets for the market battle against what they considered a slow telecommunication giant.

This drama has not occurred somewhere above Manhattan Avenues, but in a small Eastern European country.

How and why did tens of millions of Euros invested in the CRM project of Mr. S leak away like sand, into the pockets of consultants and software vendors? This question is asked by hundreds of companies that have decided to implement CRM and take a step forward to customer-oriented business.

The answer is individual for each of them. However, the legendary failures of CRM projects, such as that of Mr. S, or many others which have ended with much less glamour and lost resources, can be explained by few main reasons, summarized in an unique and fundamental mistake of introducing CRM. It stems from the complexity of any CRM initiative. It appears as its starting point, or simply impose itself through perception where “one doesn’t see the forest from the trees”, meaning that problems around complex IT solution make forget why the initiative had been launched at all, and which where the original indicators of its success.

Detailed elaboration of CRM processes, documented in a book of nearly 600 pages served as basis to the mastodon project of Mr. S. Despite millions of Euros invested in the project and its preparation, it was doomed to failure from its first day.

In summary, there where two main reasons. The first was the aforementioned implementation of CRM applications and overestimation of its power, with neglecting the fact that the business is run by people rather than processes drawn by a group of experienced consultants. But the project did not even survive enough to come into the hands of internal users to get the chance to crumble during the day to day operations. It failed already on the basic understanding of the role of IT within customer-centered processes and the respect for the compromise between the cost of the implementation and its return.

Unpretentiousness is the name of the game in the company of successful CRM implementations. Some processes, among hundreds designed to be embedded in the CRM application, were simply too pretentious. They demanded so many adjustments and use of artificial intelligence that those couldn’t succeed. Especially when you take into consideration that computer decision making involves the appropriate information, the same one that is achieved only after years of using CRM systems.