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"Business Intelligence"
I must admit I do enjoy Beck Bennett’s series of commercials for AT&T where he poses the question, “Which is better: faster or slower?” I find his deadpan approach to a variety of co-actors and situations very humorous. The question “Which is better: faster or slower?” has interesting application in today’s information and analytics environment. Faster has always been better, correct? The scenario holds true in every industry. If you can make better decisions at a faster pace than your competitor or adversary, then you will always hold an advantage over them. However, the key isn’t just faster, but better decisions faster!
An interesting event occurred last week that made the point that faster is not always better. A short-lived Twitter hoax briefly erased $200 billion of value from the US Stock Market. False reports of explosions in the White House triggered a set of algorithms monitoring news feeds into a two-minute selling spree. In this case, untethered analytics only increased the pace at which we can make mistakes and caused the DOW to drop 145 points. The error was quickly identified and the DOW bounced back, but who knows what losses were incurred by algorithms reacting to the news feed and potentially to other algorithms reacting to those algorithms.
I am fortunate to be in the information and analytics industry and am continuously astounded by the algorithms and analytics that I see people put together. However, this event continues to remind me that even the best algorithms need good data and solid IT development principles such as building in a failsafe. Perhaps we need to teach these algorithms to check their sources before taking action.
With the increasing use of social media tools like LinkedIn, Facebook or Twitter for business, organizations are starting to look at different ways they can use the information gathered from these tools to create marketing strategies, new products or even improve existing products. Social business intelligence is gathering all positive and negative comments about a company from social networking sites and leveraging this data for visual dashboards, scorecards and much more. According to Arcplan President and CEO, Roland Hoelscher*, “If done correctly, integrating social media analysis and business intelligence gets you immediate insight into web activities that have an impact on business”.
Most organizations are keen on creating a 360-degree view of their customers. In order to effectively achieve that, they have to monitor or be part of the conversations on social media sites because that’s where their customers are sharing experiences about a product or service. These organizations have to find ways to engage their customers and then figure out how this information is going to impact their future strategy. Companies like Apple do a great job at engaging their loyal customer base. The create buzz about their next device which leads to multiple discussion threads on various blog sites and trends on Twitter and Facebook. Apple uses social media as a free marketing tool and its customers are doing all the work for them – if you think about it, it’s simply brilliant.
Social media gives good insight into consumer’s buying patterns that can benefit both small and large companies. These companies can now track what their customers are buying, what they like, what they think about certain brands, and then use those metrics to create a marketing strategy or new business initiative. One of the biggest mistakes that companies make is that they want to measure every possible metric. Measuring everything creates an overload of data making it nearly impossible to act on anything. Companies need to determine the most important factors to their strategy and start from there.
* http://biblog.arcplan.com/2011/05/qa-integrating-social-media-and-bi/
As a business undergraduate at the University of Cincinnati, I recently noticed an article in the Cincinnati Business Courier about P&G’s push into business intelligence and analytics. Why are CIOs of P&G, FedEx and Boeing just now beginning the push “to make business intelligence the way that business gets done”?
Business Intelligence is already a mature market and we’re beginning to see the next “maturity cycle.” Analytics has been a top priority for CIOs for many years, but some have yet to pull the trigger. This leads me to believe that these corporations are testing the waters, waiting to jump in when the analytics market is hot enough that competition rises.
Economically we know that competition ultimately dilutes the market with firms promoting higher quality, better services and lower prices. Firms will be at the mercy of these corporations who are trying to get the lowest price for analytics services, while business intelligence firms are trying to get as much as possible without cutting into their margins.
Gartner estimates a 7 percent increase in BI software revenue in 2013 at $13.8 Billion from 2012. By comparing BI service providers in 2011 and 2010 in terms of market share and growth, we can get a general idea of where the market is headed.
| Company |
2011 Revenue |
2011 Market Share (%) |
2010 Revenue |
2010 Market Share (%) |
2010-2011 Growth (%) |
| SAP |
2,883.5 |
23.6 |
2,413.1 |
23.0 |
19.5 |
| Oracle |
1,913.5 |
15.6 |
1,645.8 |
15.7 |
16.3 |
| SAS Institute |
1,542.8 |
12.6 |
1,386.5 |
13.2 |
11.3 |
| IBM |
1,477.6 |
12.1 |
1,222.0 |
11.6 |
20.9 |
| Microsoft |
1,059.9 |
8.7 |
913.7 |
8.7 |
16.0 |
| Other Vendors |
3,363.8 |
27.5 |
2,931.1 |
27.9 |
14.8 |
| Total |
12,241.0 |
100.0 |
10,512.2 |
100.0 |
16.4 |
Will business intelligence be the star that burns the brightest? Will it become just another management methodology that will fade away like all the others?
Six Sigma seemed to work great for Jack Welch at GE but his protégés that took that methodology to other industries failed. Not every method works in every industry. Analytics has already been very successful in data overloaded industries like banking and insurance, even logistics, but how will it pan out for consumer packaged goods (CPGs) and airplane manufacturers? Most CEOs of large corporations are focused on quarter-to-quarter earnings and increased shareholder value just to keep everyone happy.
Although U.S. corporations are sitting on more cash than ever before they are more than hesitant to spend it. Apple has over $100 billion in cash, but cash won’t make a better IPhone, will it? If these companies opened up their wallets it would not be in their best interest to just throw money at their short-term problems by investing in new technology or hiring new people. Unfortunately throwing money at a problem only provides temporary relief.
On the flip side when corporations like P&G and FedEx begin to become more transparent with data and hopefully more profitable, shareholder value will rise tremendously. Business leaders understand that analytics, if implemented correctly with specific strategies and goals, will add to the business’ bottom line. For the investor time will only tell; it might be a good idea to keep an eye out for these companies by measuring performance five years before BI implementation and 5 years after.
Source: http://www.bizjournals.com/cincinnati/blog/2013/02/pg-ceo-mcdonald-business.html
Google Trends shows the term “Business Intelligence”, as a web headline topic, has declined since 2004. In the past two years it has been surpassed by the term “Big Data”. “Business Analytics” is emerging as the term some industry thought leaders, such as Gartner and IDC, are using as the catch-all term for software solutions that use data analysis to guide business decisions.
Despite the essential inclusiveness of all three terms, there is no shortage of discussion on the differences among these and a number of other contenders. Are the old terms so limited that they cannot contain the huge new advances in the field? Or have there been too many disappointments with attempts to deliver “Business Intelligence,” that we need new, exciting, and “untainted” terms.
It is important that we do not get distracted by new umbrella terms that cover the same mission, the same systems, and the same activities. It is like arguing over whether a Prius is an automobile or a car. The important thing is that there are exciting new technologies that can be applied to achieve the objectives of Analytics, Business Analytics, Business Intelligence or Big Data. It really does not matter which term is used. Let’s face it, When is BI not BI? If a term refers to ways of making data meaningful and profitable, it’s all BI.
Follow the money.
CIO Magazine’s survey of industry leaders on IT spending makes a good starting point. “The majority of IT executives are expecting budgets in 2013 to increase over 2012. While only 23% expect their budgets to shrink.” The CIO Poll is consistent with Gartner’s spending forecast which shows overall growth to be low at 3-5%, with global IT spending over 3.6 trillion in 2013.
Top business priorities over the next year include overall revenue growth, exceeding customer expectations, attracting and keeping customers, and improving quality. IT alignment with the top business priorities is driving a shift in the ways that technology is transforming business processes. There will be IT categories actually experiencing modest growth, while other categories shrink.
The category at the top of the growth list is Mobile/Wireless. It’s no wonder, when the mobile device itself can be used as the device to execute a transaction, monitor delivery of products or services, promote sales, provide customer support, as well as control manufacturing processes on site and remotely. As technology shifts, most expect the expenditures on the Applications category to continue to grow to support the opportunities presented by the innovations.
Of course the increased mobility of the company workforce, in turn, promotes the movement towards cloud based access to the enterprise IT resources. The number of enterprise organizations planning to increase budgets for outsourced IT services – which includes cloud services – is the highest since April 2012.
One of the more interesting results of the CIO poll was the response to the question, “In your opinion, how likely are organizations that have implemented tablets to adopt technologies such as cloud, social, and mobile solutions sooner than organizations that have not?” The surprise is not so much that 70% thought that this was a good indicator, but that this one question makes up 25% of the generally distributed version of the CIO Tech Poll: Economic Outlook.
The most popular phrase during next year’s networking events will be, “Say, do your employees get iPads?”
Adam Dennison, vice president/publisher of CIO, commented on the results of the poll. “While the research reveals budget increases, it is specific technology categories that are seeing growth. To meet business priorities and stay aligned with technology transformation, mobile/wireless, outsourced IT services and apps are receiving a larger percentage of the budget increases. Enterprises are in a race to grow revenue and exceed customer expectations, which can be met through technology innovation and implementation of mobile to enhance customer experience.”
Mobile and cloud technologies are driving IT from its traditional role as an information repository and processor and into the executive offices as communicator and decision support tool. IT services now reach outside the back office to touch the customer and supplier directly in ways that are just being dreamed of. The CIO Poll highlights the challenges and opportunities that IT now shares with the business it serves.
Sources:
CIO Tech Poll: Economic Outlook http://www.idgenterprise.com/report/cio-tech-poll-economic-outlook
Gartner Worldwide IT Spending Forecast http://www.gartner.com/technology/research/it-spending-forecast/
IT Organizations Focus on Revenue Growth and Forecasting Mobile, Cloud and App Budget Increases http://www.idgenterprise.com/press/it-organizations-focus-on-revenue-growth-and-forecasting-mobile-cloud-and-app-budget-increases
To learn more about how these technologies can help give your business a competitive advantage, contact LÛCRUM today.
Several weeks ago while in the office, I signed for a package; nothing unusual about that since I sit close to the front desk. What was unusual is that the package was addressed to me! With the excitement of a 5 year old, I tore at the sticky seal of the large white envelope and to my sheer delight was a box containing a Kindle Fire HD. My very own Kindle Fire in high definition! I felt like I’d won the lottery. Once the excitement wore off and I read the card from my gift giver I decided to turn it on and check it out. I removed it from its sleek box, looked for the on/off switch, and found myself becoming frustrated that it was not very obvious. After what seemed like ten minutes, I finally asked a recent college grad in our office to show me how it worked. With ease, she turned it on, showed me how to download my Facebook account, get my e-mail up and running, stream in my Amazon account so that I could buy books and told me of numerous things that could be done on this thin, little device about the size of a 5×7 picture frame. I continue to think about that moment in time and laugh at myself for not knowing what to do with a simple and amazing digital device.
My experience with the Kindle Fire HD is not unlike the experience of many companies who seek to make sense of their mass amounts of data. In a recent article published by IBM, it said that every day, we create 2.5 quintillion bytes of data – so much that 90% of the data in the world today has been created in the last two years alone. It is also estimated that less than five percent of companies are analytical competitors. So, is this a problem or an opportunity? As a veteran recruiter, I say opportunity! As data begins to grow, so, too, does the need for talent who can manage, analyze and make sense of this data. First and foremost, I look for candidates whose consulting experience relates to data and analytics. Secondly, I specifically seek those who have a compelling story. If candidates can engage me in meaningful conversation about an ETL tool or talk about how they gathered requirements in order to ultimately help build a data warehouse, I will invite them to come in to talk with us. I like the candidates who get excited about analytics and building dashboards and understand how data can change the way companies run their business. When I see that passion and the excitement of a 5 year old peeling back the sticky seal of an unmarked box, that’s when I know we’ve found our candidate. It’s the candidate who can show our clients how to turn on the device, where to find the data, what the data can do, and how the data can make their life simple and amazing.
LÛCRUM, Inc. recruits innovative, versatile, and driven individuals. Risk Takers Welcomed, Team Players Required! To learn more about current openings please contact the author, Tracy McMullen – Recruiting Manager, or send us an inquiry.
How Silver Became Gold – The Triumph of Data-Driven Behavioral Marketing & Analysis á la Nate Silver
If you have become weary of election propaganda, recriminations, exuberance and everything in between then I’m sure that you’re not alone. Every election cycle seems to be more mentally exhausting for the public than the last. However, there is one piece of non-partisan analysis from the 2012 U.S. Presidential campaign that should not be overlooked; the prediction of the Presidential election winner.
Whether you read the New York Times©, Wall Street Journal©, the New Republic©, etc., the post analysis predication discussions can all be summarized into one simple statement: Why Nate Silver was so right and why everyone else was so wrong.
If you have not heard of Nate Silver, then you can think of him as the equivalent of a Justin Bieber-like pop icon in the world of behavioral statistics and forecasting. There’s a ton of information on his background and methodology if you Google™ him, however in order to avoid turning this blog into an exegesis on Nate Silver, I think that his insight can be summarized as follows: humans can be segmented into diffuse groups by their behaviors, and these groups act in a manner that is consistent with those behaviors. Furthermore, their behaviors in these groups can be targeted and tracked. Whilst more senior political analysts were using their “gut instincts” and “past experiences” to dictate their predications, Silver used his mathematically identified behavioral groups to forecast the correct result for both the 2008 and 2012 elections.
To read the rest of this article from LÛCRUM Principal Consultant, Kaddie Abdul-Mutakallim, click here.
The topic of Productivity Improvement Using Data-Driven Decision-Making came up in a discussion with some colleagues and I wanted to learn more. The phrase also goes by DDDM or D3M or just plain DDD. The term sounded familiar, so I expected to find a starting place in Wikipedia. The message that Wikipedia came back with said “The page ‘Data driven decision making’ does not exist. You can ask for it to be created. . . ”. Now, I was curious.
Further research found that DDDM is now most commonly used in education and has emerged as the generally accepted approach to meeting the government requirements in the No Child Left Behind programs. The private business sector has noticed some of the successful productivity improvements in education and the process of applying relevant aspects of DDDM to business is underway.
Data-Driven Decision-Making is a process of collecting and analyzing multiple types of data from multiple sources to guide a range of decisions to achieve stakeholder goals. Data is involved in determining the goals, determining what decisions need to be made, as well as acting on the alternative choices with the best expectation for success.
The top results from a Google search on data driven decision making in business provided the web pages where I selected the quotes that I am passing on in this post. These quotes tie together, at least in my mind, the way Data-Driven Decision-Making, while anchored in the truth of the data, still demands the intersection of the humanity, creativity and goal setting behaviors of the decision-makers and the stakeholders who will live with the results of those decisions.
Here are some quotes I found interesting:
*For each quote you will find the title of the page, the author of the article or post, the source of the quote, when different from the author, and a link to the web page. For some I have added a comment you will find enclosed in brackets with my name [Dennis . . .].
I am starting off with a quote made almost 10 years ago because it is a timeless essential to any approach to making data meaningful.
Everyone’s talking about D3M. Use this guide to help prevent all that data from driving you nuts. (2003)
By: Pamela Wheaton Shorr who in turn quotes Robert Ewy, director of planning at Community Consolidated School District 15 in the suburbs of Chicago. Ewy runs MicroStrategy’s decision-support tools on an IBM data warehouse. [link]
“He says that rather than having the answers all the time, educators need to come up with the right questions.”
Ten Reasons to Use Data-Driven Decision Making
By: A.J. Riedel [link]
“The third reason:
Reduce the number of suboptimal decisions being made by your managers.”
Intuition vs. Data-Driven Decision-Making: Some Rough Ideas
By: Bob Sutton [link]
“I make every decision with my gut. My gut, however, makes better decisions with the benefit of fact-based input. So it’s a matter of applying the discipline of passion-free analysis, then trusting your judgment.” November 03, 2009 at 08:34 AM
[Dennis: the quote is a comment from an unnamed individual]
Five Steps For Making Data-Driven Decisions
By: Jon Bruner; who in turn quotes Piynaka Jain head of a company that conducts analytics training. [link]
“How are you building alignment so that when you say ‘here are my insights, ta-dah!’ somebody is going to be able to work along with you.”
[Dennis: Interestingly, in the whole article the only reference Bruner made to a decision under consideration was the analyst’s choice of methodologies.]
Show Me the Numbers
By: Anna Brown; who in turn quotes Race Against the Machine by Erik Brynjolfsson and Andrew McAfee [link]
“… the two conclude that ‘weak human + machine + better process was superior to a strong computer alone’ and, more remarkably, ‘superior to a strong human + machine + inferior process.’”
[Dennis: Peter Dorrington commented on Anna’s Post]
“…it is people that bring the art and the insight to the science of analysis and this is a potential source of value-add (not cost reduction) that can drive revenues in these global markets.”
Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?
By: Erik Brynjolfsson, Lorin Hitt, Heekyung Kim; quoting Kohavi
“As a Microsoft researcher memorably put it, ‘Objective, fine-grained data are now replacing HiPPOs (Highest
Paid Person’s Opinions) as the basis for decision-making at more and more companies.’ (Kohavi et al.,
2009)”
[Dennis: To access this article enter “How Does Data-Driven Decision-making Affect Firm Performance?” In a Google search box and select this article which you find it in the search results.]
Study: Marketers Stumble Over Data-Driven Decision-Making
[Dennis: The article quotes a recent Corporate Education Board (CEB) Report that found that the type of marketers who rely more heavily on data for making everyday business decisions are actually under performers. ] [link]
“The problem, in this case, is a tendency to make ‘data driven’ decisions without really understanding what the data means. As a result, the report concluded, these marketers often overreact to changes in the data they review and lose sight of their long-term goals.”
If you want more information on how LÛCRUM can help your business make better decisions based on your data, contact us today.
In my prior posts, I have touched on the three “V”s of Big Data: Volume, Velocity, and Variety. These are the foundational defining characteristics of Big Data. It is precisely these that bring to light the importance of what has been proposed as the fourth V: Veracity. Petabytes and Zettabytes streaming in at sub-second rates, in dozens of formats from hundreds to billions of sources is the stuff of Big Data nightmares. Technology is continuing to advance to help us mitigate those issues.
Now that we have all this data we have to ask the pivotal question; can it be trusted? This is the essence of Veracity. Veracity is defined as “conformity with truth or fact,” or in short, Accuracy or Certainty. Things that can cause us to question the data are inconsistencies, model approximations, ambiguities, deception, fraud, duplication, spam and latency.
All of the Big Data we are collecting and processing has one real purpose, to assist us in making meaningful decisions. The data must be truthful, or the decisions we make will not be valuable and could cost not only money but also potentially lives in the case of Hospital ICU sensors and monitors.
My younger brother was born under circumstances that caused him to be transferred immediately to a neonatal ICU. In the same unit, another infant had been born with all of his organs outside his body. He had survived surgery and his vital signs were being monitored with a pressure mat in his incubator. On one visit, his father lifted him out of the incubator and noticed the monitor was still registering the baby. He gave the child to the mother, placed a stuffed animal in the incubator, and covered it with the blanket. You can imagine what happened when the father called the nurse to check on the baby and she discovered a stuffed tiger instead.
Variability is another Big Data “V” that has been proposed which directly affects Veracity. People often confuse Variability with Variety. Say you go to an ice cream parlor that has 20 flavors of ice cream. That is Variety. Now, say you go there three days in a row and order strawberry; but each time it looks and tastes different. That is Variability. Variability in the data we are analyzing can have a profound effect on the believability of the business decisions we making.
I am not suggesting that all data must be 100% truthful and accurate in all scenarios. If the data is being used for exploratory, experimental, or mining purposes, there may be a tolerance for inaccuracy in the sample. The volume, velocity, variety, and variability of Big Data may preclude us from taking the time to cleanse it all thoroughly. We need to understand the allowable level of uncertainty or lack of Veracity in the data and re-define trust in the context of the business questions we are attempting to answer. We need to weigh the cost of that uncertainty against the Value the data brings to the business.
In the past four posts, I have explored Big Data Volume, Velocity, Variety, Veracity and Variability. The final “V” of the Big Data phenomenon is Value. The simple truth is that the Value of Big Data comes through Analysis and the application of the results to specific business needs.
Want more information on how LÛCRUM can help provide you with Big Data solutions? Contact us today!
Christopher Burney (1917–1980), a British author and spy during WWII wrote of his 18-month captivity in a POW camp “Variety is not the spice of life. It is the very stuff of it.” While Volume and Velocity present formidable challenges in Big Data acquisition and storage, it is the variety of sources that “is the very stuff of it.”
Traditional Business Intelligence systems source data primarily from consistent, structured, operational systems built on relational database platforms. These systems are required to manage transaction data in strict, predictable, repeatable, auditable ways. Their function depends on the lack of variety in the data. Increases in data volume and velocity can be handled with increased technology volume and speed; storage, memory, and processors.
Looking only at a company’s transactional, structured, system data tells the internal story. This data can be used to answer many verification questions; typically justifying a hypothesis. Additionally we can look for patterns in the data to help predict behaviors or discover trends. Structured data is what we typically classify as being stored in relational databases as rows and columns.
When we begin to look beyond the walls of the corporation, at the outside influences on profitability, marketability and growth, such as consumer sentiment, competitive economic and market pressures, meteorological impacts, etc. we move beyond the “structured” world and step into the “unstructured” world. This is where data variety begins to become a challenge.
Some examples of the variety of unstructured data formats are emails, PDF documents, word documents, videos, photographs, power point presentations, social media posts such as Facebook wall posts and comments, Twitter Tweets, customer service call and chat logs, web pages, search box submissions, etc.
Consider a retail company attempting to asses and analyze Customer Sentiment. How many places do people post reviews about companies? There are specific websites such as Yelp, TrustedPlaces, BooRah, RateItAll, dishola. You may also leave feedback on virtually every online retail website. Feedback is posted in combinations of free form text, Leichardt scales, stars and others. Additionally, there are the millions of call log and chat records from customer service centers.
Not all of this data fits neatly into rows and columns. Much of it needs to be handled by alternative technologies. A sizeable list of Big Data Technologies can be found at nosql-database.org.
An intriguing application of big data variety is Turnitin.com; the leading plagiarism identification and grading provider. Turnitin.com is used by over 3,500 Universities and 6,500 secondary schools in 126 countries worldwide. I personally know of a student who, pressed for time, decided to cut and paste together a research paper from multiple web sites and PDFs. It was only a rough draft and she needed the grade. The teacher required the paper be submitted using Turnitin.com. As soon as the student clicked the submit button the system returned her “Originality Score” which indicated that the work was 92% plagiarized. We have become conditioned to nearly instantaneous responses so this does not seem too amazing until you consider that her paper was compared against over 24 billion websites, 250 million student papers, 110,000 published documents, 300,000 dissertations and theses in fractions of a second.
Another example of Big Data Variety is comScore; an Internet analytics company providing marketing data and analytics to many of the world’s largest enterprises, agencies, and publishers. comScore tracks and measures people’s activities as they navigate the internet by analyzing data from desktop internet traffic, mobile device activity, email, geo-positioning data, and multimedia combined with census data. comScore processes over 2.1 billion tag records per hour.
So far, I have explored the three “V”s of Big Data, Volume, Velocity, and Variety. There have been many “fourth” V’s proposed such as Variability, Veracity, and Value. In my next post, I will look at these other V’s.
Want more information on how LÛCRUM can help provide you with Big Data solutions? Contact us today!
- LÛCRUM is an information, intelligence and analytics consulting firm comprised of business and technology professionals focused on making data meaningful.
