Good read. Find out what other small businesses are using to mine big data they can afford and use.
Excerpt: When Kobie Fuller was hired as chief marketing officer at Revolve Clothing, in July 2011, one of the major challenges he faced was how to encourage customers to become repeat buyers.
He was sure that the answer lay somewhere in the mass of customer-order data the 160-employee online apparel retailer collects daily. But when he arrived, that data was stored in a difficult-to-navigate database. Pulling sets of data meant asking an IT guy to run a special query. And even figuring out where to start was tricky. Revolve’s data reached back to when the company was founded, in 2003. “I wanted ready access to key statistics without begging someone to mine the data for me,” says Fuller.
Read full article via Big Data for Small Businesses | Inc.com.
A good look at today in information technology. With changes and new requirements, what will survive and grow, what will become obsolete and die?
Excerpt: As a result, “20 to 40 percent of businesses at this conference won’t be here in five to 10 years,” said Forrester Research VP and Principal Analyst Bobby Cameron. “The ones that disappear are the ones not able to stabilize back office activities that don’t differentiate them competitively.”
As technology practices struggle to align with changing competitive business practices, they must also incorporate the arrival of big data, analytics, social, mobile and cloud technologies in their processes and architecture.
Whether through planning or as a result of the evolution, captive enterprise IT skills are migrating from blue collar to white collar.
Read full article via Surviving Digital Disruption – Information Management Online Article.
Good read. I think the article helps to put data in perspective for the rest of us.
Excerpt: You recently launched Hack/Reduce — a sort of Big Data playground — in collaboration with MIT, Harvard, and other Boston-area universities. What was the inspiration behind it?
Fred Lalonde: We started Hack/Reduce in response to two big barriers to using Big Data. One is scalability. You can’t do Big Data on your laptop. You need 1,000 computers. The second is finding people who have the training, knowledge and expertise to work with Big Data. Even in a city like Boston, with the depth of software talent, there was literally nowhere where people with an idea could boot up enough computing power to try it.
We started with a one-day hack-a-thon and invited people to come over. The premise was you say to geeks: “We’re booting up all the computing power you need to come and do whatever you want.”
Read full article via What Could You Accomplish With 1,000 Computers? – Dana Rousmaniere – Harvard Business Review.
Here is some great advice on your “beginnings” with Big Data — start small and test the results for an optimum use for you, thus a cost effective way for small business on a tight budget. You can then scale or adapt to more and new as “knowns” are put into place.
Excerpt: While it isn’t hard to argue the value of analyzing big data, it is intimidating to figure out what to do first. There are many unknowns when working with data that your organization has never used before — the streams of unstructured information from the web, for example. Which elements of the data hold value? What are the most important metrics the data can generate? What quality issues exist? As a result of these unknowns, the costs and time required to achieve success can be hard to estimate.
As an organization gains experience with specific types of data, certain issues will fade, but there will always be another new data source with the same unknowns waiting in the wings. The key to success is to start small. It’s a lower-risk way to see what big data can do for your firm and to test your firm’s readiness to use it.
Read full article via To Succeed with Big Data, Start Small – Bill Franks – Harvard Business Review.
Good read and a reality of big data best practice goals. Recommended.
Excerpt: Big Data can allow us to see and predict human behavior objectively. What starts small — for instance, seeing through data how people really eat and move — can become massive — such as overhauling the health care system to address real behavior.
I am optimistic about Big Data, but I’m also realistic. There are many obstacles to getting to a good place with it. Here are some of them:
Read full article via Big Data’s Biggest Obstacles – Alex “Sandy” Pentland – Harvard Business Review.
Good read and takeaway how-to. Big Data for many is overwhelming, particularly when it comes to management and utilization. However, in order to remain competitive, we need to develop best practices and the more information we have of “discovered” tips and advice, the better.
Excerpt: Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. In a recent Harvard Business Review article we explore how companies require three mutually supportive capabilities to fully exploit data and analytics: an ability to identify and manage multiple sources of data, the capacity to build advanced analytic models, and the critical management muscle to transform the organization.
Getting started on a successful data and analytics journey, however, is a continuing challenge for many leaders and they often struggle for a clear strategy that ties data and analytics to improved performance. We took a close look at companies that have recently launched big data strategies to shed further light on the tough road C-level executives face. From these experiences, we have distilled four principles to defining a strategy and getting started:
Read full article via Get Started with Big Data: Tie Strategy to Performance – Dominic Barton and David Court – Harvard Business Review.
Good advice as we all trudge through the evolution of data management and data utilization of Big Data for the ultimate best practices tips to help our small businesses.
Excerpt: One can’t help being impressed with the effort biologists, physicists, and other scientists devote to data quality. From careful design of experiments and data collection processes, to explicit definition of terms, to comprehensive efforts to ensure the data are correct, no effort is spared. This is not surprising. After all, data are the lifeblood of science.
Increasingly, data are also the lifeblood of business and government. And the attention science pays to data quality provides important lessons, especially for those interested in “Big Data.”
Simply put, bad data make everything about Big Data — from discovering something truly novel, to building a product or service around that discovery, to monetizing the discovery — more difficult. The two most important problems are:
The data are poorly defined, leading to incorrect interpretations.
The data are simply wrong, incomplete, or out-of-date, leading to problems throughout.
Read full article via How to Repair Your Data – Thomas C. Redman – Harvard Business Review.
With all the articles on Big Data, especially this year, if you haven’t heard of the new expert, Data Scientist, you will. This article gives you some insight into what is a data scientist and why you will need them. Data mining, data management, information technology
Excerpt: There are some great articles about big data and analytics in the current issue of HBR, and I am happy to have co-authored one of them with DJ Patil, one of the world’s first practicing data scientists. In fact, data scientists are what our article is about. I would argue that they are the most important resource for capitalizing on big data. While there is a lot of Hadoopalooza in the technology press about the tools for managing big data, and they are wonderful, it’s also true that they are a) widely available, and b) mostly free. Neither can be said of data scientists. The other necessary resource, massive quantities of data, can also be found on every virtual corner these days. If your customers have internet access, for example, you’ve got big data.
Simply put, you can’t do much with big data without data scientists. They are the magicians who transform an inchoate mass of bits into a fit subject for analysis. God may have been the first to produce order out of chaos, but data scientists do it too, admittedly on a smaller scale. They can suck data out of a server log, a telecom billing file, or the alternator on a locomotive, and figure out what the heck is going on with it. They create new products and services for customers. They can also interface with carbon-based lifeforms — senior executives, product managers, CTOs, and CIOs. You need them.
Read full article via Can You Live Without a Data Scientist? – Tom Davenport – Harvard Business Review.
I doubt anyone would argue with this point — we are all still looking for best practices in the evolution of Big Data metrics and utilization. For all leadership and management
Excerpt: Big data is great. But we should consider that we’ve actually had more data than we can reasonably use for a while now. Just on the marketing front, it isn’t uncommon to see reports overflowing with data and benchmarks drawn from millions of underlying data points covering existing channels like display, email, website, search, and shopper/loyalty — and new data streams such as social and mobile engagement, reviews, comments, ratings, location check-ins and more.
In contrast to this abundant data, insights are relatively rare. Insights here are defined as actionable, data-driven findings that create business value. They are entirely different beasts from raw data. Delivering them requires different people, technology, and skills — specifically including deep domain knowledge. And they’re hard to build.
Even with great data and tools, insights can be exceptionally tough to come by
Read full article via Metrics Are Easy; Insight Is Hard – Irfan Kamal – Harvard Business Review.
A webinar for help and how-to use Big Data — the effective data management — for gain.
Excerpt: Historically, companies have decided which markets to focus on and have allocated sales resources based on looking at past results and using gut instincts. But today, “big data” and deep analytical capabilities give sales and marketing leaders a better way to make decisions. Managers now have the ability to use data to precisely identify lucrative micromarkets and better align sales resources to maximize opportunities. Companies that use analytics to pursue a micromarket strategy are dramatically increasing sales without spending more.
In this HBR webinar, McKinsey partners Manish Goyal and Homayoun Hatami describe how to use data to drive sales — without increasing costs.
Check out webinar here via Mining Big Data to Find New Markets – Jason Sylva – HBR Events – Harvard Business Review.
Recommended for information technology management and small business management — I think this begins to address some of today’s challenges in changing roles — what is expected, what is needed, what can be accomplished.
Excerpt: Most IT organizations are focused on incremental innovations and cost savings. They listen to customers, aiming to meet their needs and solve their problems.
Michael Schrage, research fellow at the MIT Sloan School of Management’s Center for Digital Business, offers a different and challenging perspective. He describes IT as “Innovation Technology” and sees digital media platforms fostering product and process transformation.
In this Harvard Business Review webinar, Schrage discusses new technologies — from cloud computing to machine learning to Big Data — that are transforming companies. He discusses technology’s role in innovation, transformation and creating new value for customers, and describes the cultural changes that need to happen to make digital investments more productive.
Listen to webinar discussion via IT Conversations That Matter – Jason Sylva – HBR Events – Harvard Business Review.
A short but important list again on the challenges of Big Data.
Excerpt: Key Questions To Ask Yourself Before Embarking On A Big Data Journey
Read full article via Key Questions To Ask Yourself Before Embarking On A Big Data Journey | Forrester Blogs.
Another good article on BIG DATA — what it is and what it is not — harnessing and best practices uses. With the evolution of overload information and technologies driving more — most, including small businesses, are striving to “get their head around” the challenges and the benefits.
Excerpt: By contrast, the overwhelming majority of enterprise IT systems can’t quite make up their digital minds. Is big data there to feed the algorithms or inform the humans? Is big data being used to run a business process or create situational awareness for top management? Is big data there to provide a more innovative signal or a comfortable redundancy? “All of the above” is exactly the wrong answer.
What works best is not a C-suite commitment to “bigger data,” ambitious algorithms or sophisticated analytics. A commitment to a desired business outcome is the critical success factor.
Read full article via What Executives Don’t Understand About Big Data – Michael Schrage – Harvard Business Review.
Good read — if you are not struggling with these issues yet — perhaps you have just opened the doors and are not into “business” quite yet — or you live under a rock.
Excerpt: The prospect of employees downloading and mashing up data brings up concerns about data security, reliability and accuracy. But in my research, I’ve found that employees are already assuming more responsibility for the technology, data and applications they use in their work. Employees must understand how to protect sensitive corporate data. And leaders will need to learn to “trust, but verify” the analyses of their workforce.
Ensuring that big data creates big value calls for a reskilling effort that is at least as much about fostering a data-driven mindset and analytical culture as it is about adopting new technology. Companies leading the revolution already have an experiment-focused, numerate, data-literate workforce. Are you ready to join them?
Read full article via Data Is Useless Without the Skills to Analyze It – Jeanne Harris – Harvard Business Review.