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Data, Strategy, Leadership, and Innovation

Tag: Big Data

Don’t Go on a Date with Data, Marry It

There are a lot of companies out there today sitting on several types of data, with various levels of integration. For most, it’s a process involving multiple individuals to extract, transform, and develop that information into a useful dashboard or decision-support system given the company has legacy infrastructure and several different third party vendor platforms all producing useful information but it’s in own specific format.

The big challenge then, that companies face today, is how to tie all of this information together in a way that provides meaning but more importantly, provide it in a way that can be near real time with the ability to tie different types of data from all over the enterprise together.

Yes, you know how much you lost and earned in a given period of time, but can you determine why with the ability to stitch together different data sources? Can you do your own regression analysis, or perform correlation research to determine what trends might be causing a rise or decline in monthly revenues? Or do you have to hire it out, and wait several weeks, while analysts inside or outside your company work to produce the answer to that one simple question?

There are lots of people willing to charge you for the ability to do this month to month, on an ongoing basis, but if you’re content with having systems kept in disarray while you’re driving up the cost of labor to mine it every time you have a question about your numbers, then you’re simply dating your data.

You’re content leaving data at arm’s length, getting to know it to a point, but you’re not spending the time and effort it takes to really get to know the data inside your company and make the investment to make data a key part of your professional life. Marriage means accepting the ugly truth about someone, and acknowledging to them the same sometimes ugly truths as well, to build a close relationship.

Marrying data means you’re accepting the truth that legacy infrastructure, silo’ed data sets, and weeks spent building a single dashboard isn’t working for you and you’re ready to spend the time and effort it takes to bring data closer to the core of your business. It means making the investment to build an agile analytics platform that allows for ad-hoc analysis, and spending the time it takes to get your leadership team on board with understanding what a regression is and what level they feel comfortable being trained to help drive insights without an army of external analysts.

Marrying data also means accepting the things you can’t change about it, and learning to live with limitations as they are. It doesn’t make sense to send everyone to school to learn data science, but it does make sense to get up to speed on what it means and getting people trained in the vocabulary of data so that those highly trained resources can build what is needed and make sure everything you’re investing in has a clear return at the other end.

Making the ultimate commitment to data, vs having an off again on again relationship, means you’re wiling to spend the time it takes to make the upfront investment to clean up the silos and tie together the systems keeping your really valuable insights locked up. It means knowing that it’ll take time to see the value, but it’ll be worth the investment, vs continuing to grow your OpEx budget on consultants and FTEs working with what’s there now, and spending time sorting and tying each system together for one-off requests.

More importantly, marrying data means you’ve accepted that the key to a happy company is a happy data warehouse, and that Innovation in it’s most meaningful way means you’re able to draw out from your past what solutions and ideas might help fuel your future. By spending the time and effort to build meaningful data interconnectivity, along with the systems necessary to analyze and understand that data, you’ll be able to see what trends are coming your way and how you can be proactive to meet the challenges in an ever changing industry environment.

You’ll reduce the risk of being disruptive, you’ll be armed with answers before the questions get asked, and you’ll be able to walk hand in hand with your data into the sunset while every division within your company gets insights they need to better track what’s working and what isn’t along with driving new revenue streams to your customers.

A happy marriage for some seems like a fairy tale, and it’s not going to solve every problem you encounter. However, if you’re willing to put in the time it takes and accept that there’s things you could be doing to pay better attention to your data along with spending the time to care of it, your data will produce insights and become more open to analysis as a result which will always work better than keeping it in a chaotic state and spending time doing one-off reports.

So consider what marrying your company data looks like for you, and build a plan and a roadmap to make data more meaningful and acceptable for your company. I can guarantee you, your competitors are probably already doing the same.

Dan Maycock is the author of “Building The Expo”, which shares best practices on leveraging #Innovation in meaningful ways and saving the concept from it’s overused but underutilized past. The book has first hand stories, and best practices from Dan’s years of experience working with Fortune 1000 companies dealing with emerging technology adoption in an increasingly dynamic business environment. You can purchase the book atAmazon.com or learn more about Dan at http://www.transform.digital

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Why Everyone Should be a Data Miner

In thinking about the topic of data mining, a lot of different types of roles pop up in people’s minds. From data scientists typing away in giant data centers, to DBAs sitting in cubicles processing large amounts of corporate data, to an analyst building a spreadsheet for an annual report contribution.

Maybe it’s something far more physical, bringing up images of pick axes and hard hats and a big block of data (however that’s visualized, probably with 1’s and 0’s – all matrix like). Regardless of the image that comes to mind, it’s probably hard to fathom every business professional in some form or another becoming adept at data mining, and considering it a critical competency to keep in their professional toolbox in the years to come. Yet, when we explore the topic, we can easily see how data mining could become one of the preeminent skills that set folks apart in an era where it’s harder and harder to stand out from an increasingly noisy and competitive work climate. Lets start by looking at the six attributes that make up data mining (as defined by Wikipedia)

  • Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  • Association rule learning (Dependency modeling) – Searches for relationships between variables. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
  • Regression – attempts to find a function which models the data with the least error.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Though the definitions seem somewhat dense, think about how you’d be able to take any job – from being able to use regression analysis to construct a real estate data model to improve pricing predictions, to using summarization to build a better financial report for your senior leaders to interpret how great of a quarter you had.

Though some methods of data mining are harder than others, and you can quickly get in way over your skis without proper learning, knowing how to sift through data, and pull out the useful stuff, will give you a greater sense of the world you work in by understanding the data that matters and it’s so easy these days to learn data mining techniques online!

Just typing in “data mining classes online” produces hundreds of leads, from Coursera to MIT open courseware. Though some options go into areas like Data Science, which is much deeper level analysis, it all starts with understanding data and how best to derive meaning from it – regardless of how deep into the weeds you want to go.

This in turn gives you a big foot up against your competitors, who are largely relying on other services / people to hand them processed data and conclusions to do something with. Going from a commodity to a distinct competitive advantage means going in a direction others aren’t, and just having a nicely worded dictionary isn’t enough these days – you need to be able to turn that dictionary into a novel, and tell a story with the data that will reveal things about your business or your industry that’ll drive better decisions through unique insights.

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