Category Archives: 99-Applying business intelligence

Conducting systematic research, making the best use of available resources, going beyond the simple and obvious sources, critically evaluating quality and reliability of evidence

07b-Using good information design, 99-Applying business intelligence, 99-Spreadsheet modeling, charts & graphs, tables

What is the Best way to Pull data from the Internet? #GrowthHacking

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A lot of times we find ourselves in the need to pull a database of information online, but copy and pasting (for example a government database) would take too long.  This forces us to either abandon the data infusion that might give us great insight or to make bad assumptions.

As with many of these types of problems there is a startup working on it.  This specific problem is solved by Import.io.  Their slogan is:

Web Data Extraction Made Easy,  Transform any website into a table of data or a Structured API in minutes without even writing any code.

This software really does what it says and is super simple to use.   Turn any website domain or page into an excel document!  While this is probably a big win in productivity for a lot of us, dont miss the big picture.  There are a bunch of these type of programs available that will make your work easier, but, often times they are either hard to find or we are just not aware that there is a solution for it.

I recommend everyone sign up for these services two newsletters: Product Hunt and Beta List.  They will keep you on the bleeding edge of whenever a new problem solving software comes out!

 

 

 

01-Structuring a problem, 99-Applying business intelligence

To Make a Better Bet, Use Trial and Error

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Sometimes we expect things to turn out perfectly the first time. If you do not, then you may be one of the the few. In order to (hopefully) have our ventures turn out the best as possible, we tend to brainstorm and plan ahead to circumvent any issues we may run across. However, it has been suggested in a Harvard Business Review post by Josh Sibel that we use trial and error.

Some of the greatest innovations we benefit from are offspring of trial and error. We have our ideas, notions of proper execution and their outcomes. Essentially, we plan to perfection. As eloquently stated, “perfection is often the enemy of success,” and the pursuit of such perfection sometimes proves to be a detriment. Trial and error allows one to learn from their mistakes and create a product that could have a better outcome than the one that we planned to be perfect.

We think of Apple as the bastion of perfection, but though it appears to be a well oiled perfect machine is not without the use of trial and error. Think about it, just about every iPhone release or iOS release has not been without subsequent modifications to improve the product.

 

99-Applying business intelligence

Applying Business Intelligence: The Power of Business Resource Groups

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Business Resource Groups, often also referred to as Affinity or Ethnic Resource groups, are an important component of organizations. For those of you who aren’t as familiar, business resource groups are typically composed of a group of employees who share a commonality (e.g. race, gender, sexual orientation, age etc….) Ten years ago, corporate diversity was seen more as a good thing to have, but not as a necessity for corporate growth. Leaders often categorized business diversity initiatives with corporate giving and office philanthropy.

Today, company executives now realize that in order to survive and succeed in today’s competitive business environment, they have to bring diversity to the forefront of the company. The top executives and the employees have come to the realization that the introduction of different voices, with different perspectives, backgrounds, priorities and orientations will help identify opportunities that will yield success in new markets.  Here is an example of how one company applied this business intelligence, and saved $100 million in market research fees using its company’s business resource groups. Resource Groups & Marketing: DiversityInc Innovation Fest

Does your organization have business resource groups? If so, have they been used to help drive business results?

Source: Resource Groups 101: A Primer on Starting Them & Using Them for Business Goals

 

05-Creating & constructing good solutions, 07a-Crafting an effective formal presentation, 99-Applying business intelligence

What Makes Messages Stick?

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In his book, Presentation Zen: Simple Ideas on Presentation Design and Delivery, Garr Reynolds discusses many different methods and exercises that help you craft a great presentation, including techniques used by TED presenters.

A chapter I find particularly helpful talks about “messages that stick”- why do some presentations make a great impact on you whereas others just fade away minutes after you have stepped out of the conference room.

In order to make a presentation memorable, take a step back, prior to crafting your slides, and think about a time when stories were passed along around the campfire. What made those messages resonate with you? Garr mentions six principles, first introduced by the Heath brothers in their book Made to Stick:

  • Simplicity. Decide what matter in your presentation and simplify these points. Not everything should be “high priority”.
  • Unexpectedness. Keep the crowd interested. Create a “gap” in their knowledge by asking questions, then fill that gap with information.
  • Concreteness. Give a simple speech with real examples, not abstractions. For example: “let’s kill two birds with one stone” is easier than saying “let’s work towards maximizing our productivity by increasing efficiency across many departments”.
  • Credibility. Most of us are not well-known experts in our field, and usually use data to back us up. Try putting the data in contexts instead of just leaving it “as is”. For example: “enough battery to last you on a flight from NYC to LA” instead of “five hours of battery life”.
  • Emotions. An easy way to help people “feel something” about your content is to add images.
  • Stories. Try to provide real-life examples and illustrations instead of simple streams of information.

Think about the last memorable presentation you have witnessed. Most likely, it encompassed most, or all of the points above.

A link to the book on Amazon:

http://www.amazon.com/Presentation-Zen-Simple-Delivery-Edition/dp/0321811984/ref=sr_1_1?ie=UTF8&qid=1404828019&sr=8-1&keywords=Presentation+Zen%3A+Simple+Ideas+on+Presentation+Design+and+Delivery%2C+2nd+Edition

 

 

 

06-Building a case, 99-Applying business intelligence, business databases, evaluating evidence, library research

Data credibility challanges impact all BI research

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The posts concerning data credibility are absolutely spot on – but their relevancy doesn’t stop with challenges around hard data.

Reading through them, the observations that jumped out:

  • repercussions if the information is inaccurate
  • need to analyze and ensure the integrity of the data
  • shifting focus on going to the actual source of the data rather than applying a Band-Aid approach to the bad data
  • Chevron’s use of a process to ensure that the right decisions are made using the right information

All of these can be applied to the entire business intelligence process, including the identification, access and analysis of secondary information, including data.  Think about data you locate in industry trade publications, in Wall Street analyst reports, even in sources of market research, such as Marketline/Datamonitor, Euromonitor, or EIU.  You can’t just take the data for granted, assume that it is correct.  You need to question, probe and understand many facets, including the originating source of the data, methodologies around the data gathering process, the accuracy, integrity and bias of the source of the data (both original and published) which includes understanding something about the intended audience and purpose/motivation for the data creation/use as well.

And like Chevron, you must have a process (remember the  discussions around the necessity for “Planning the Work” and “Working the Plan” in your fall’13 MP class and the spring’14 BI workshop).  Having these in place provides you and your team with a framework that, if applied correctly, helps ensure that you avoid using bad data, using unsubstantiated insights for your analysis and recommendations.

Last Fall in MP, Professor Noonan introduced you to a few BI frameworks, and we briefly mentioned a few more in the BI workshop.  I am attaching more detailed discussions around 4 of these frameworks (what we are calling Business Intelligence Briefs) which speak so directly to your data credibility threads.

Business Intelligence BRIEFS-4_Information Gathering Plan Business Intelligence BRIEFS-3_Four Avenues for Gathering Intelligence Business Intelligence BRIEFS-5_Owners of Information and Motivations Business Intelligence BRIEFS-2_Five Dimensions of BI

 

05-Creating & constructing good solutions, 99-Applying business intelligence, business databases

Data’s Credibility Problem for Business Intelligence Users

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Data driven decisions are the basis of finding solutions that will solve any problem. Utilizing the BI tools are an everyday affair for me, which can often have repercussions should the information be inaccurate. Before we can apply BI to decision making, there is a need to analyze and ensure the integrity of the data.

HBR had a good article regarding the time lost due to looking, identifying and correcting errors in data sets. Time is of essence when it comes to project deadlines and there is nothing I rather not do, than waste time. Companies need to meet deadlines and they give there full trust in data sets. If an error comes up at the last minute when conducting analysis, we often look to quickly fix the data set without fully addressing the root causes.

Previously working in the retail clothing environment, many issues would come up regarding data integrity of our systems. Much of the blame would come back to the IT team and they would try to fix it themselves without going to the respective department who owned the data set. Not only is this inefficient but does not encourage collaboration and communication.

The solutions to this issue is better communication between the data creators and users. Too often do we put a band aid on a mistake and never go to the source. The focus should be shifting the responsibility from the IT team to the managers of data sets. This article gives examples at Chevron and the processes we should be implementing to ensure the right decisions are made with the right information.

I invite others to see if they have utilized these techniques in their companies through their management. What works best for your teams when these issues arise?