As you read this, please imagine old, ‘90s Chicago Bulls “let’s get ready to rumble” intro. Better yet, turn on Guns n’ Roses’ “Paradise City.”
This week’s BI post is about the top 50 articles about business intelligence available online. Period.
Why should you go through such a long list?
These articles will inevitably supercharge your understanding of big data and business intelligence, where the industry has been and is headed, and will aid in any search forbusiness intelligence software you might have.
I’ve divided this list into various BI categories for your ease: BI Statistics and Broad-View Trends, BI: What It Is, What It Does, Why You Need It, Cloud BI, BI Apps, BI and SMBs, Machine Learning, Big Data, Data Governance, Data Visualizations.
Articles are organized in order of basic information to more advanced. The earlier a piece is in the section, the more foundational/basic the information is. In a few cases, this means the harder reads are up front, but I’ve tried mostly to organize sections in a way that will lead easily through the concepts.
Whodoesn’tlike statistics? They’re like maps: they give you a broad idea of where you are, where you could go, and the challenges of going in that direction. Here’s few pieces that give you sense of the BI and big data landscape. Most are statistics-based, but some are just good overviews.
First takeaway from this piece: there are an estimated 5.4 million people now building cloud apps. Oh, and a quarter of all global software developers aremakingapps in the cloud, too.
What’s the relationship between how your business analyzes big data, and how well your business uses your IoT devices? There is a correlation, but you’ll have to read the article to find out.
Louis Columbus’ articles impress me this much:
The most telling takeaway from this piece: 26% of the respondents surveyed in the quoted research plan to hire employees with business intelligence skills in the next 12 months. Only two skills were rated higher: help desk and programming (there were ten skills total on the list). Beyond that, the #1 IT department priority (read to find out) is what business intelligence is all about.
Heads up, incoming IoT market frustration and market contraction. The frustration will happen unless IoT hype turns into real-life deployment. The contraction will happen because the big guys are going to consume the smaller businesses, or the smaller ones will cease to be. Also, look for security to be a big focus for businesses everywhere, particularly after that DDoS hack last October. This article’s also a great intro to business intelligence terms (like “edge,” “semiconductor,” and more).
5. Bernard Marr, 20 Mind-Boggling Facts Every Business Leader Must Reflect On Now
A useful piece for gauging how much data exists today, and how much more will be created in coming decades. Here’s a taste: by about 2021, there will be “over50 billion smart connected devicesin the world,” all of which will consume, and create, data.
6. Joe McKendrick, With Internet Of Things And Big Data, 92% Of Everything We Do Will Be In The Cloud
There are plenty of suggestive stats here (the amount of information sent over the cloud may increase by a factor of 3.7 by 2020), but there’s also some solid reporting on what those stats suggest. Joe McKendrick’s analysis also discusses whether the public or private cloud will dominate in upcoming years, and whether these developments will help or hurt the SaaS market.
The articles in this section parse business intelligence a little more deeply. Where the stats and figures of the previous segment enlighten BI’s context, the articles below investigate what makes business intelligence, business intelligence. The first three pieces, from Gartner, are all paywall protected, but will give you the best understanding of the essence of business intelligence you can find anywhere.
Gartner’s Magic Quadrant is the best overall view of the BI marketplace in existence; vendors see it as a major distinction to make the Magic Quadrant. Gartner ranks major BI software by completeness of vision, and ability to execute. Rankings on both categories place vendors in one of four quadrants: Niche Players, Challengers, Visionaries and Leaders. Along with this is an in-depth review of the strengths and weaknesses of each player who made the Quadrant. Required reading if you’re looking toinvest in BI tools.
The Critical Capabilities (the document, and the actual capabilities) are what Gartner uses to place vendors on the Magic Quadrant. This is another useful source of information, and it’s designed to dovetail onto the Magic Quadrant.
The Evaluation Criteria tells you how to judge a BI program. An essential overview of what every Business Intelligence program should have, broken up into Required, Preferred and Optional categories. If you’re in the market for business intelligence software, the “Evaluation Criteria” will help you vet the quality of potential vendors’ programs.
10. Chris Liebetrau, Business Intelligence. What is it and why is it changing business?
Several reasons to like this piece: it introduces the concept of BI, expands on that definition, and provides specific examples of how BI helps businesses in a tactful fashion. By “tactful,” I mean that this is a company blog, and those sometimes spend more time selling to the reader than informing them. Contrarily, this post delineates what makes BI valuable, but doesn’t overload the reader with stats and examples purely there to puff up the company’s ego.
11. Ronald Van Loon, What Is the Future of Data Warehousing?
No list of BI articles would be complete without a piece by industry maven Ronald Van Loon. Van Loon has eighteen years of experience in the field, has written countless articles about BI, andDataconomynamed him “one ofthe Top 10 Global Predictive Analytics Influencers.”In this article, Van Loon tackles data warehousing (how, and where, your data’s kept). The data you use in business intelligence all comes from data warehouses, whether those are servers you own, or servers in the cloud. This article’s useful because it underlines the relationship between data warehousing and BI, and provides an expert’s thoughts on how data storage and access will change in the coming years (hint: data’s going to be more like Netflix; read to see how).
Most business intelligence software is now either in the cloud, or being built for the cloud. There are non-cloud options, but they’re going out of style. Here are some pieces to get you up to date on the BI/cloud relationship (hint: it’s cozy).
I’ve put this one first because it’s the most general view of BI’s benefits for any business. It doesn’t immediately bog itself down in statistics, but sticks to broader analysis about the ways cloud-based software can help you, like the ease of updates, the the better security. It’s a pithy example of a common, BI-evangelization type of article, and it’s one that would probably be great to forward to a decision-maker, too.
AutomileMarketing VP Jay Krishnan’s discussion of how the cloud can increase your SMB’s revenue will intrigue you, especially her ideas on how an SaaS solution can help even a reluctant business make a big change.
Consider this article a “For Dummies” guide to the cloud for complete newbies. Only, I found Griffith’s style more readable than any of the Dummies guides I’ve read. Not only will you learn what Cloud is, you’ll learn more advanced concepts, too. By four paragraphs in, you’ll actually know what NAS (network attached storage) hardware is, and how it’s different than the cloud.
What else can a cloud-based (read: internet-based) BI program do? Provide accessibility. That’s the argument behind Ben Tai’s look at how cloud software “helpsremove the responsibility of BI from the IT department task list.” Of particular interest is his point about the easy integration that comes with cloud products. No one wants their new software to not play nice with the existing software. It’s like when a sitcom introduces a new character no one likes who throws everything off (I’m looking at you, this season ofThe Walking Dead).
16. Paco Darcey, The Analytics Software Learning Curve—How Steep Should You Go?
Make no mistake: there is a learning curve for BI software. How steep a learning curve should you accept? That’s the question Paco Darcey addresses in this piece from Smart Data Collective. It’s a useful guide to someone in the middle of shopping, or even to someone considering BI (you’ll want to know what to avoid, or prepare for).
17. Joe Stangarone, 6 key areas to examine in any BI solution
It’s natural to be excited about BI, but don’t’ let that keep you from shopping smartly. Check out this brief guide from Joe Stangarone to get a sense of the basics any BI app, or program, should have. This piece is also useful for Stangarone’s dissection of the categories. For instance, you’ll want to make sure your “self-service” BI solution is fully self-service (read: you shouldn’t need to code to use it).
Normally I can’t stand slideshow articles, and this is one. However, I had to overlook my dislike because Lisa Morgan’s article gives good advice about business intelligence, and gets it from BI experts and industry figures. Tip 5 about actionable insights is one every CEO, manager and employee should keep in mind when using a BI program.
19. Gartner, Strengthen Your Mobile BI Initiatives WIth These Ten Best Practices
If you want to make the switch from a desktop-limited BI program to one available on mobile devices, this piece of research will tell you what to expect, how to make the switch, and the challenges in that (also Gartner, also paywall protected).
Trying to decide between BI programs? If you’re looking at SAP or Oracle, or even if you’re not, this guide to both programs can help you figure out which is better for you. If you’re completely new to BI, the comparison of features will highlight what you should look for in a program. There’s also a little information from Gartner’s Magic Quadrant, as a plus.
This guide is similar to the above, but focuses on Tableau and Tibco. Both are great options, but, again, one might be better for your business’s particular needs. Check particularly the section on mobile apps, as easy collaboration and access are two of the key benefits employees get from business intelligence.
One way to view the broad change between old, enterprise-style BI and new, self-service BI is that the little guys have increasing opportunities and power. Old BI is kind of like Alexander Hamilton’s view of the US as a new country in need of guidance, with the big boys at the top dominating the conversation. Self-service BI is like Jefferson’s view of the small, educated citizen farmer as the key player. Only today’s farmers are culling data, rather thanwheat.
From BI bigwig Bernard Marr (hisbooksare stellar), here are six simple sources of big data to empower your business intelligence initiatives. His advice to use services like Trendera and Twitter are particularly easy; you could probably start using those this afternoon (or whenever you’re reading this).
23. Pratik Dholakiya, “How Small Businesses Can Make the Most of Big Data”
How can you make your current data valuable? Follow the four tips Dholakiya outlines, like the fact that you should “focus on collaboration and boosting team confidence, rather than technology alone.” The tips in this article are also a good introduction to the sort of broad benefits BI offers.
Is there actionable info hidden in your Facebook likes? Surprise, surprise, the answer’s yes, but Chris Sapardanis’ interview with Mastercard elaborates on the answer. For example: the information about your customers’ zip codes can lead to higher revenue, if you know how to use it.
One of the big divisions in the history of BI is between the older, more expensive programs once available only to the Enterprise, and newer, cloud-based and SaaS solutions that make BI accessible for SMB’s. Dave Hochman’s piece surveys this difference, and provides a few specific examples of how SMB’s have already benefitted from BI.
26. Xander Schofield, 3 Ways Small Businesses Can Double Profits with the Cloud
Pretty self-explanatory. Three ways switching to a cloud-based solution can make your SMB money. The second point about website uptime is one that would help any manager sleep better at night, and it’s also a good reminder of just how costly downtime can be. The third point will make both managers and employees happy.
27. Brent Leary, Rick Jackson of Qlik: Analytics Can Be Applied To Almost Every Single Aspect Of Your Business
This interview withQlikCMO Rick Jackson is great for two reasons: it’s a refreshingly accessible look at the jargon-filled world of BI from a thirty-year veteran, and it gives concrete examples of how your SMB could use BI to save some money, today. Jackson’s thoughts on using BI to monitor inventory and sales performance are the sort of things that will make managers wish they already had BI.
Machine learning, a subfield of artificial intelligence, is going to be this year’s hot topic. Or at least, so many people say so that it’s a good idea to be familiar with the concept. Machine learning is the broad term for algorithms that allow a computer to learn on its own. They’re being used to teach autonomous cars to tell the difference between, say, a plastic bag and a pedestrian.
28. Jeff John Roberts, Here’s What Companies Get Wrong About Machine Learning
If you want to know what something is, know what it isn’t. If that sounds a little weird or counter-intuitive, it was a philosophical approach popular with the Medieval theologians (negative theology). Jeff John Roberts’ review of a panel at last fall’sStructure Security conferencedispels a few of the common mistake people make: machine learning is the same as artificial intelligence, machine learning takes away the need for human involvement, machine learning will make dollar-bill quilts out of your data (that stuff has to be cleaned first).
29. Mike Yeomans, What Every Manager Should Know About Machine Learning
I always trust a writer who tells you, upfront, that things are more mundane than what the hype suggests. Mike Yeomans begins his introduction to machine learning this way, and the quality of his explanation inspires trust until the last sentence. He explains what machine learning is, what are some common uses of it, and even why the shape of data makes machine learning necessary.
Paramita Ghosh’s article is a step up in complexity, but given that Gartner says a mixture of AI and advanced machine intelligence will be one of 2017’s top-ten tech trends, it’s worth digging deeper. Focus especially on her sections on how ML is will contribute to the democratization of BI, and how the algorithm economy will drive this trend.
31. Cathy Reisenwitz, How Businesses are Using Machine Learning and AI in 2017
My colleague Cathy Reisenwitz has written a dynamite introduction to machine learning that simultaneously discusses where it’s already being used. She explains what it is, and, even more importantly, when it’s actually useful for a business. This one’s also a great source for cool machine learning stories. One company’s even used machine learning to figure out when a customer is most likely to open an email.
Big data isn’t synonymous with BI, but it’s closely related. You use BI to get valuable infoout ofyour big data. Big Data’s the mother lode of valuable information, and business intelligence tools and strategies are your miners and mining equipment. Yes, some is better than others: Tableau or Tibco are like high tech, modern mining tech, while just using Excel is more or less panning for gold.
If you sign up with MIT Sloan’s Business Review, you can access three free articles a month. That’s an amazing deal, given the usual costs of academic articles. “Lessons from becoming a data driven organization” analyzes four cases where businesses turned their data into money. The presence of the “lessons from” in the title should tell you that becoming data-driven isn’t a simple process, but the stories here will help you in your journey.
A good primer on what Apache Spark is, and what it does. A little dense if you don’t know the topic, but the breakdown of three reasons why Spark is so useful, this piece is a must-read.
If you’re unfamiliar with Big Data, some of the terms might seem off-putting. However, Anadiotis explains concepts like the end of Hadoop, or how data platforms are diversifying in dizzying dimensions.
Data governance is all about keeping track of your data, whether that means making sure it’s safe, or making sure you know where it is. Here are three data articles on the topic to get you started, and even improve your data governance strategy, if you already have one.
A basic look at cybersecurity misconceptions, like ““Small businesses must staff expensive IT professionals to defend against cyberthreats.” There’s a way around that. Read to find out.
36. Eran Levy, Why You Should Already Have a Data Governance Strategy
If you’re new to data governance, read this. Eran Levy’s as good a teacher as he is a thought leader, explaining the basics of data governance, while suggesting helpful why’s and how’s for your data.
37. Nicola Askham, The Interview: What It Takes to Succeed In Data Governance
Ashkam interviews Collibra’s data governance expert Shamma M. Raghib. Raghib’s experiences are a useful primer on the topic, especially her resource recommendations, and her illustrative story about her biggest data governance challenge.
Sensors are the new traffic cameras: they’re everywhere, and they’re a great source of revenue. Only, where your local municipality uses traffic cams to fine you for turning left on red (IT WAS ONE TIME), the sensors you can use in your business can bringyourevenue. Business intelligence software that integrates with your sensors can turn the data you collect into money.
38. Adi Gaskell, Predictive Maintenance and the Industrial Internet of Things
Tech expert Adi Gaskell describes three companies making money with the data their things (usually sensors) on the Internet of Things collect.
39. Preston Gralla, IoT and the data-driven enterprise: How to dive into the data flood
Want to know how to start turning your IoT devices into money? Preston Gralla’s piece is a great starting point. Though he’s talking about larger, enterprise-sized businesses, strategies like the one GE uses to divide equipment into three action categories could be used by a business of any size. As an added bonus, Gralla also discusses what platforms you’ll need to crunch that IoT data, and the challenges that platform will face tranforming that unstructured data into value.
Speaking of platforms that turn IoT data into value, Paul G.’s article highlights an issue with them: those platforms don’t know how to talk to each other. Especially since there are “300 to 400 IoT platforms,” this could make all those lucrative predictions about the IoT (“GE Digital forecasts a $60 trillion market for IIoT”) less of a possibility.
41. Ben Dickson, What makes IoT ransomware a different and more dangerous threat?
Heard ofransomware? It’s malware that sneaks onto your computer and screws things up. Want it gone? You’ll have to pay. Ben Dickson’s discussion focuses on what parts of the IoT are likely at risk (Industrial Internet of Things, hospitals, and power plants), and which ones are less so (smart homes and connected cars are less likely targets, for now, at least).
You know what I love about going to the movies? When the poster isn’t an image, but a well-reasoned and cogent paragraph about the potential value of the film. Said no one, ever. At least, not 35% of the population, becausethe other 65% are visual learners.
Data’s no different. Data in spreadsheets is a speed bump: you’ve got to slow down, read, and analyze. Data in visualizations is like nitro in the engine: it boosts the “drive” of your business processes.
42. Jeff Pettiross “Why the Time-Tested Science of Data Visualization is So Powerful”
A good BI platform lets you visualize your data (a visualization’s worth a thousand stats). But that’s not a new insight. Check out this cool piece on data visualizations that date back to 1300s China. And though Jeff Pettiross doesn’t mention it, you could arguably call theLascaux cave paintingsdata visualizations…
Points first for the phrase “Excel hell.” If Dante wroteThe Divine Comedytoday, the icy plains of Hell’s bottom layer would be massive spreadsheets. Data visualization, however, helped lead the utility company National Grid out of hell by helping them figure out which overdue customers were most likely to pay. In other words, a good data visualization clarified the surest sources of money.
44. Geoff Hoppe, 16 Free and Open Source Data Visualization Tools To Grow Your Business
If you’re looking to make your data into a gripping visual, but don’t want to spend any money, check out this list. The options run from great, simple organizational tools like Augl, to more complex tools like MyHeatMap, that let you make an impressive heat map of, say, customer demographics by region. At least one of the tools in this article can make your data into something immediately gripping.
45. Data Storytelling: Why Visualization is Only Half the Story
I’m tagging this one because it’s a good hype antidote. Data visualization is an amazing tool, but you’ve got to know how to use it as part of a bigger data story. Otherwise, data visualizations are like illustrations without the words. And as much as kids like picture books, your boss may not feel similarly.
While I hope that the above list is useful to you, there’s only so much I’ve come across so far.I’d value every extra set of eyes available helping me, and anyone reading, keep track of the world of business intelligence. If you’ve read, or written, a great piece on BI recently, please let me know in the comments below!