Currently viewing the tag: "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.
As business travelers and Cincinnatians we have all witnessed firsthand the ever-increasing airline ticket prices from Northern Kentucky International Airport. All companies, big and small, are trying to keep down unnecessary costs. This visualization was created in Tableau Desktop in order to show how data visualization can make it easier to view trends and patterns of airline costs in order to cut costs. This type of analysis and visualization can be done in any industry in order to view the progress of the company against their goals and performance indicators.
Why are visualizations so useful and why create one? Visual.ly Blog was recently asked “Why is Data Visualization so Hot?” They responded saying that, “Visualization allows access to challenging data sets, it allows exploration, can be fun, and provides useful information in an efficient way.” I would agree and add that, as humans, we are already trained to recognize trends and patterns in graphs, which is why they are so efficient in translating data.
Some of the questions that I wanted to answer about round trip airline prices were:
How do airline ticket prices fluctuate over time?
Looking at this graph it is easy to visually see that some trip prices vary a lot and some are fairly constant. This led me to another question:
Do prices follow trends by departure airport or arrival airport?
Looking at the graph you can see that for San Francisco the ticket prices seem to follow the same general trend. The other arrival cities were generally the same as well.
Would it be worth it to fly out of the Columbus (CMH) or Dayton Airports (DAY) instead of Cincinnati?
Looking at the graph above I found that CVG was on average $50 more expensive than CMH or DAY airports.
How competitive are the airlines by round trip? Does the cheapest flight change airlines constantly or do they stay the same?
Using this visualization you can choose a trip to look at in order to see how often the color (airline) changes. If the color changes a lot then that is a competitive trip where the cheapest airline changes often. However, trips like CVG to Charlotte and Las Vegas are constant by airline.
If there is a cheaper airline which one is it and how many flights do they have?
The graph above shows the average price of an airline ticket (color) and the total number of flights recorded (size of circle). Overall, looking at the graph, Delta had the most round trip tickets but in the end the average price was the highest. Whereas on the other end of the scale, AirTran had the cheapest average prices but only a few tickets. In other words, the deals that AirTran does have are very good deals.
Lastly, everyone’s favorite question:
When should I buy my ticket in order to get the cheapest price?
Normally when this question is asked it seemed that Tuesday was the best day to buy airline tickets, but after looking at my data, it showed that Wednesday had the cheapest prices. However, by looking at the graph you can see that the price difference is not very significant.
Using Tableau made it very easy to view the trends and patterns in airline prices. It was easy to see the fluctuations of trip prices and compare airports. Data visualization is a hot topic and can greatly help your company to quickly find anomalies and progress of performance indicators.
**Sources:
http://blog.visual.ly/why-is-data-visualization-so-hot/
This data was collected by finding the cheapest round trip ticket in March regardless of day, length of stay, or airline using www.hipmunk.com.
Wikipedia defines Forecasting as the process of making statements about events whose actual outcomes (typically) have not yet been observed.
Examples of forecasting would be predicting weather events, forecasting sales for a particular time period or predicting the outcome of a sporting event before it is played.
Wikipedia defines Predictive Analytics as an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns.
Examples of predictive analytics would be determining customer behavior, identifying patients that are at risk for certain medical conditions or identifying fraudulent behavior.
Based on these definitions, forecasting and predictive analytics seem to be very similar…but are they? Let’s break it down.
Both forecasting and predictive analytics are concerned with predicting something in the future, something that has not yet happened. However, forecasting is more concerned with predicting future events whereas predictive analytics is concerned with predicting future trends and/or behaviors.
So, from a business perspective, forecasting would be used to determine how much of a material to buy and keep on stock based on projected sales numbers. Predictive analytics would be used to determine customer behavior like what and when are they likely to buy, how much do they spend when they do buy, and when they buy one product what else do they buy (also known as basket analysis).
Predictive analytics can be used to drive sales promotions targeting certain customers based on the information we know about their buying behavior. Likewise, the information obtained from predictive analytics can be used to influence sales projections and forecasting models.
Both, predictive analytics and forecasting, use data to achieve their purposes. But, it’s how they use that data that is much different.
In forecasting, data is used to look at past performance to determine future outcomes. For instance, how much did we sell last month or how much did we sell last year at this time of year. In predictive analytics, we are looking for new trends, things that are occurring now and in the future that will affect our future business. It is more forward looking and proactive.
So, although forecasting and predictive analytics are similar and closely related to one another, they are two distinctively different concepts. In order to be successful at either one, you have to have the right resources and tools in place to be able to extract, transform and present the data in a timely manner and in a meaningful way.
A common problem in business today is people spend much more time preparing and presenting information than they do actually determining what the data is telling them about their business. This is because they don’t have the right resources and tools in place.
At LÛCRUM we have the resources, strategies and tools to help businesses access, manage, transform and present their data in a meaningful way. If you would like to learn more about how LÛCRUM can help your business, visit our website or contact us today.
We are coming into the golden age of analytics. This is the vision that the speaker – CEO of a company that develops data visualization software – illuminated for the audience at a recent customer conference. “We are putting the power of data into the hands of creative people to explore the worlds of possibility.”
The idea that we can use our talents to solve client data puzzles is like an adventure that makes it fun to come to work every day. We’re explorers in an unknown land, moving from all the standard questions (and the standard answers) to a place where the questions themselves haven’t been formulated yet. New thinking, contemporary sensibilities, and breakthrough technologies are disruptive factors in this age.
One area I pay attention to is the continued evolution of business intelligence (BI) in a mobile environment. Mobile BI received a lot of fanfare in the past year, and all the major platforms promote a mobile solution.
Now that some of the hype is starting to settle down, what is the real story? Here are a few thoughts based on my own observations and experience.
1. Mobile BI deployments will favor use on tablets, rather than smartphones, given current screen sizes.
It’s easy to refer to “mobile” like we refer to “Europe” as a single form factor or entity. The reality is more complex, as there is a range of devices from the smart phone, with relatively small screen sizes, to tablets, with screens just a bit smaller than you’d see on a typical ultrabook laptop. More screen space gives us more room to place data and provide interactivity. I see organizations prioritizing tablet deployment over smartphone deployment in most cases.
2. Business gains so far have been incremental, providing efficiencies rather than game-changing breakthroughs.
Many mobile BI efforts seem to focus on converting the oodles of reports hanging around every corporate office into a mobile format. That makes data more portable, which is an improvement. But what we should seek to do is to make the analysis and decision-making that goes along with running a business happen in a portable way too – where you are, right now, as soon as information becomes available and action is needed. This concept of “right-time mobile analytics” (not just mobile BI) is where I believe transformational gains will be found.
3. Design principles need to evolve to better anticipate the needs of mobile tablet users.
Most mobile BI efforts seem to be focused on cramming the dashboard or report that was designed for a desktop user into a mobile screen. There are several problems with this approach. First, a dizzying array of formats, resolutions, and screen sizes is present in the market. It’s reminiscent of a challenge with website design, where you don’t know what kind of screen the user will have for their desktop.
Beyond screen size, you quickly discover that dashboards and interactive visualizations that are crammed onto a mobile device are balky to navigate when you substitute fine-point control of a mouse cursor with the more generalized notions of a tap or swipe. Analysts and designers should invest the time to redesign existing visualizations and reports so that they can be easily and efficiently used with these new human interfaces.
4. Mobile BI tools do best at providing answers to known questions, rather than providing a platform for rich and interactive data exploration and discovery.
Largely due to the factors and challenges related to the interface, we have not seen a good mobile implementation of the interface needed to explore the data and design new visualizations. Sleek interfaces that enable and facilitate data discovery, such as the forthcoming update to Tableau’s Desktop Professional software (v8), are getting there on the desktop, but are very limited on their mobile implementations.
I’m not sure we should even care, because this may be a square-peg-in-a-round-hole problem. I don’t see the compelling need right now to port that capability to mobile, when most of the value in mobile will come from deploying effective, efficient visualizations to those who need better information to make right-time decisions, rather than enabling analysts to design new analyses on-the-go (and burning through their data plan in the process).
5. Standardization on a single mobile platform can significantly reduce development timelines.
This will help reduce complexity and allow you to dip your toe into the water with less up-front investment. Fewer permutations of screen size, operating systems, and wireless carriers will reduce the time needed to deploy your solutions (little secret: this is one of the reasons that vendors originally delivered on Apple devices – you could predict how your software would look to the user!).
Apple, with its line of iOS devices, has the best track record in this area. They have smartly positioned with a very small number of screen size variants across the iOS hardware platform. And several studies have also shown that historically, users of iOS devices generated a significant majority of all mobile device traffic over any other platform. Therefore, I believe that an engaged user base and a streamlined development lifecycle will favor adoption of mobile analytics solutions that operate on iOS devices.
6. Support the rollout of cellular-enabled mobile devices throughout the enterprise for knowledge workers that can best leverage mobile analytics applications.
Yes, this makes each tablet more expensive, and yes, cellular data plans are not free. But neither is the lost time fumbling for the client guest intranet login, or roaming the highway off ramps looking for a coffee shop with free WiFi. The investment from all of this valuable data and analytics applications will be reduced if your knowledge-based workforce cannot connect where they work, live, and travel.
Looking to the future, I believe we are now well positioned to generate competitive differentiation through mobile, BI-integrated, right-time analytical applications. The growing maturity of mobile BI platforms, and their support for the little known-capability known as write back, has the potential to be a turning point for the field. Write back in the context of BI gives the user both the ability to consume data through their visualization or BI application and to generate data that is put back into the database. This is the next secret sauce.
The actual capability to do write backs has been around for a long time. It’s even built into Excel and can be used with Microsoft Analysis Services OLAP cubes, if they are configured for this purpose. It’s also a part of enterprise BI tools like MicroStrategy as well
This is a big deal, because now we can combine analytical data (and the processing power of real-time analytics engines) with information that is entered by a user, in context, on site, in the moment while they are working on a particular problem.
Let’s say you’re a medical supply sales representative, and you go on site to visit a hospital client. Your mobile BI solution provides you with historical purchase patterns. Then, you conduct an inventory check, inputting the data while you are standing in the supply room. That information goes back to the database, and the purchasing models apply past history, seasonality, and metadata about trends at your other healthcare clients in that area (think: regional flu outbreak), generating a purchase forecast and preliminary order. The solution also recommends a product change, from buying individual packages to large count bulk packs, which would save the customer $10,000 this year. You review with the administrator, make a few adjustments, and you’re done.
This simple vignette may seem far off, but it isn’t a dream. Right-time analytics applications can become critical competitive differentiators for current and future market leaders. The complexity here is in gathering the data, understanding behavior, and building the analytical models that will help us optimize processes in our daily work. It’s complex, but definitely within reach for those that are willing to invest the time and effort to see it through.
Let’s start with a little quiz:
Hadoop is
a) Twitter shortcut for “I HAD it, but OOPS I lost it”?
b) The latest dance song craze (Macarena, Gangnam, Hadoop)?
c) A stuffed toy elephant?
d) A software solution for distributed computing of large datasets?
The correct answers are actually c) and d). You see, Hadoop is a software solution developed as part of the Apache project sponsored by the Apache Software Foundation, and it was named after a stuffed elephant owned by the son of the framework’s founder, Doug Cutting.
But what exactly is Hadoop and how does it work?
Per the Apache website, “Apache Hadoop is a framework for running applications on large cluster built of commodity hardware.” This open source software framework enables the developer/user to manage large amounts of data (Big Data) using a distributed file system. The power of Hadoop lies in its ability to leverage distributed clusters of computing hardware. It does this by leveraging two key technologies.
The first is the Hadoop Distributed File System (HDFS); a distributed, scalable, and portable file system. It is written in Java specifically for the Hadoop framework. A key component of HDFS is the name-node. This is a single server that tracks all the other nodes in the distributed client/server cluster. In other words, the name-node is the directory of who all the distributed clients are and which files each contains. As clients and files are added to the cluster, commands update the links to these new nodes in the name-node.
The second key technology leveraged within a Hadoop implementation is that of MapReduce. MapReduce is a programming model for processing large datasets. It works by enabling a master node (the node assigned the processing request) to break apart the work request into smaller sub-tasks, and send the sub-tasks out to worker nodes. This is the “Map” aspect as the master node is mapping out the workload to the worker nodes. As each worker node completes the assigned sub-task, it ships the results back to the master node. The master node then takes all the worker node results and combines them into one result set; thereby completing the assigned request. This is the “Reduce” aspect.
It is important to note that for very large or complex requests, worker nodes can also MapReduce their assigned tasks into smaller sub-tasks for their worker nodes. You could refer to this as Big Data outsourcing. As each node determines another node is better equipped to handle a portion of an assigned request, it relegates the work to a more efficient worker node, while retaining responsibility for getting the completed assignment back to the master node.
Sources:
Webopedia http://www.webopedia.com/TERM/H/hadoop.html; Wikipedia http://en.wikipedia.org/wiki/MapReduce; Hadoop Wiki http://wiki.apache.org/hadoop/
To learn more about Hadoop solutions contact LÛCRUM today.
“The pace to achieve Meaningful Use has been break-neck for many institutions. There have been primary care providers who have implemented and attested to Meaningful Use in as little as 90 days. Hospitals on the other hand, who typically implement more complex systems, have implemented and attested to Meaningful Use in as little as 6 months.”
To understand the full potential of data, you must understand where it all comes from. Healthcare in 2013 can avoid the DRIP syndrome and avoid penalties through proper use of intelligence and analytics technologies.
LÛCRUM’s Healthcare Solutions Principal Consultant, Susan Melton, discusses the solutions to collecting big data and driving business decisions and patient care through a more analytical process.
Follow this link to read the entire article.
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.
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.
- LÛCRUM is an information, intelligence and analytics consulting firm comprised of business and technology professionals focused on making data meaningful.
