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Data, Strategy, Leadership, and Innovation

Category: Data Science

Posts related to Data Science (R, Hadoop, Stats, etc)

3 Steps to take Data Analysis from “Well, That’s Interesting” to Revenue Impacting

Data is hot right now, and it seems everywhere in business these days there is another tool or framework on how to leverage data to drive a real difference in your business.

A good metaphor to understanding data effectively though, is training for an Olympic event (great timing for the metaphor, eh?). Most athletes train, not for the sake of training, but most likely because they want to stand on the podium with a medal as a statement to how good they are at the event. Anyone can train for the sake of training, but competing and winning is really the whole culmination of 4 years of tireless preparation and training.

The same is true for data visualization, in that the real goal should not be building dashboards and pretty charts, but pointing to the direct impact that data had on driving a top or bottom line impact on the company’s revenues. Yes, in larger companies it’s very hard to make a difference on the overall number, but every piece of data should tie to some positive contribution or else what is the point?

Yet, with so much data being made available, and so many people learning how to mine data for insights, there’s a lot of very pretty pictures out there which don’t move the needle at all. Yes, it’s great to hear about athlete’s and how they train, but the credibility isn’t there to the same degree as it is if they’e wearing an Olympic medal. If you’re a budding data analyst, or seasons chart builder, imagine if everything you built had a number next to it that said “this piece of data drove this quantifiable business impact”.

Though data visualization can serve qualitative benefits, such as monitoring a key business process or helping change a perspective on a topic, there should still be some way to tie even those things back to the difference it made (or could make) on the business.

Here’s three steps then, to help that mindset along

1. Understand the Reliability and Structure of the Data You’re Using 

All because you have data, and have access to data, doesn’t mean it’s useful or all that important. You having access to log files from a server, which spits out information on usage patterns of your e-commerce system throughout the day only matters if you’re able to impact that usage in some meaningful way, then tie it back to a positive revenue lift. More importantly though, you have to know the data is reliable and understand how to model it in a way that it produces accurate conclusions. Build some baseline metrics, and measure against numbers you know are correct before going into any complex modeling exercise. Once you put a chart in a slide, it’s out there. So make sure you’re starting from the right data set to begin with. 

2. Develop a Series of Hypothesis about Your Business

Once you know the data you’re working with, and have a good sense of how reliable it is, think about the business as it relates to parts you can actually control / influence. If you’re in advertising, don’t focus on product improvements. If you’re in product development, don’t worry about retention patterns on the website. Think about 3-5 gut instincts you have about how the business could operate differently, then use the data to test out those theories. Don’t simply mine the data, hoping the magical insights just pop out at you. Data, like a car, helps you get to where you’re going – it won’t take you there on it’s own. You need to at least have a rough idea of what you’re looking for, so you can build worthwhile visualization to help vet that hypothesis into an actual conclusion.

3. Focus on Low Hanging KPIs, then Expand from There 

It’s easy today, with technology making more data accessible with less effort, to try and go after ground breaking insights. However, you no doubt have areas of your business you can directly impact and know will make a sizable difference to the bottom line, that you need help in influencing. Start with thinking through 3-5 KPIs coming from your hypothesis that you can build from the data you’ve vetted, to influence key business people or back up your assertions with a new direction you’re moving your team in.

If you can measure it, you can prove it (as long as it’s reliable and true), but remember that it’s never a silver bullet or the whole story. Data can be powerful, but it can also lead people astray or get people focused on the wrong things. When done right though, using validated data tied to a hypothesis and measured in a way that drives a meaningful revenue outcome, it can make a big difference.

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Drive Revenue Growth Without Driving Off the Road

Data – it’s something everyone is hearing about these days, whether it’s IBM stating how twitter can help you build better products to Google talking about self driving cars saving the environment through the power of data-driven route optimization.

That’s all well and good, but when you’re sitting at your desk looking at the list of things you have to accomplish today, and bring about new ways to grow revenues or build efficiencies into your business, where does one even begin?

No, you don’t always need a small army of data ninjas (though that can sometimes help) nor do you need a lot of high priced tools and solutions to help find buried gold in your troves of silo’ed business data.

To really drive to change, and begin leveraging your data to drive revenue it starts with the following steps

A) Start with the right hypothesis

If you don’t know what questions you’re trying to ask, then it’ll be very hard to find what you’re looking for that’ll help you achieve your business objectives. Asking questions like “where should I sell my goods” or “what products should I build” would require very expensive, intelligent systems capable of translating english into problems that computers could try and solve along with the tribal knowledge and understanding of the industry you’re in. Those systems exist, but man are they expensive along with the IBM consultants you’d need to hire to go between you and the outcomes.

Instead, focus on a specific question based on the types of data you know your business has. Start with things you think you know about your business, like what your key demographic is or where you source your raw materials from. Then go deeper and ask why those folks are your core demographics, or why you went to that one country for zinc. Good data mining starts with understanding the problem space better, and exploring things at a granular enough level that you can understand where intuition, guessing, or laziness came into place vs finding the right outcomes at the right level. You tell me you sell products in Seattle to women 44-55 years of age, I’ll ask what neighborhoods is that least or most true and if that answer is biasing you from growing your customer base because you’re focusing on metropolitan areas from a broad national study you paid a firm to do 5 years ago.

Sure, the same answer might be true, but having those metrics and answers in place means you’ll be able to see the shift and know when those answers are no longer the case, more importantly it’ll cause you to ask why the answers are they way they are and those levers or foundational factors will become more obvious and allow you to get granular enough to spot the outliers biasing your answers which get lost in the high level aggregated dashboards most execs use today.

B) Understand where your data lives

If you live and die by your profit and loss statements, or your quarter earnings reports, chances are that there is a complicated network of data analysts and administrators that compile all that information together to come out with a single answer. If you are getting your core business metrics from the same group you’re measuring against outcomes, be careful about unintentionally biased data that leans on rounding up vs rounding down and know where that data is coming from.

Too much data exists today, and decisions get made by people along the way on how that data is compiled and delivered, so build a culture of transparency and make sure you don’t have data points stacked on top of data points where errors can slowly creep in.

Aside from transparency, agile systems built the right way means you can do ad hoc reporting and build your own metrics with the ability to drill up and down without having to wait weeks for someone to compile a report on only the question you asked. Too much legacy infrastructure, and data scattered across the company along with an over reliance on key information being locked away in spreadsheets means a mess for really getting down to the bottom of things.

C) Figure out how the data is (or is not) related

If you’d like to see how twitter is affecting your supply chain, spend a little time thinking about how the two inter-relate. Twitter is going to be something tagged by date and location at a high level, but is a bunch of key words and a user name so prepare to invest in interpretive systems that aggregate and analyze or figure out ways twitter data might tie to a critical business system. There’s lots of ways to get at the answer, but high quality dashboards with pretty graphics may be just interesting and not at all useful if you don’t have the right data behind that tool giving you meaningful answers. It’s not about big data, it’s about meaningful data.

D) Ask an expert (whether or not you intent to hire one)

Data, like engineering or medicine, is a very complicated space that gets increasingly complicated by the day. Rather than becoming a data scientist yourself, find someone you know and trust that works with data and use them as a sounding board to run your ideas and suggestions by. It’s not that you may have a bad idea on how to leverage data to achieve business insights, but having it structured in the right way while learning what’s possible and what isn’t without a lot of investment is important to finding meaningful, bite sized ways to leverage data without breaking the budget and overspending for fancy whiz bang data systems.

E) Start small, grow big, track and measure along the way

The most important thing is to not bite off a big problem, like how do you end world hunger, but something small you know you could get good insights around relatively easily, such as where you spent what and how that goes against what you make with the ability to drill down into where that changes. If you typically get profit and loss statements saying you’re profitable in washington state, understand what city that is and is not the case and why one would be different than the other. Sometimes it’s just getting more granular data to what you already receive that can have the biggest insights into your day to day business.

At the end of the day, data isn’t a silver bullet, but it can make a difference in a big way when approached the right way. Start small, build a meaningful hypothesis, and strap in for the revenue growth that will follow.

Daniel Maycock is the Director of Strategy and Analytics at Transform, a data services
company. Our mission is to help drive impactful outcomes with data for our clients. We do this by providing tailored solutions that help people get tangible applications from their information.

His new book, Building The Expo, was published in January, 2015.

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4 Ways to Effectively Use Data In Your Job

With all the excitement around how companies are using data today, it’s hard for anyone outside of a job specifically dealing with data to know how to effectively use it for their day to day work. Yet, there isn’t a single career that isn’t impacted by the use and understanding of data, and the more effective someone becomes at harnessing and understanding data mining, the more they can impact the things that impact their professional ecosystem.

From impacting your online brand, to better tracking variables you deal with around a given task at work, knowing how to leverage data can make a big difference in any number of careers.

1. Start with a question

Before diving into a number of articles or tools regarding data, start with the question of what you’re trying to answer. It sounds basic, but you’d be surprised how often I’ve worked with people that have said data is the answer without first having the question. Figure out what are the most pressing business problems you, your boss, or your company are facing and see how data might help provide insights to answering those important need to knows.

2. Start with a small amount of data, build from there

It doesn’t take petabytes of data to answer questions, sometimes it can be a relatively small set of data to answer big questions. With all the hype around big data, sometimes it’s hard to realize that with only 100 or so records, and a pivot chart, you can get to important answers that are far more useful than what a million records could show, depending on the type of data and the question you’re looking to solve.

3. Leverage third party data that’s free

There’s a TON of data out there that’s completely free, and useful to use. US Census is a great place to start, and there are a number of sites, such as Google’s public data directory that’ll let you explore it. Furthermore, you can download the data for free and combine it with your own internal data to add greater context for things like taking your company’s store sales by zip and seeing how demographic trends within those zip codes may impact certain buying habits.

4. Learn about Data Mining 

The key to making data useful is by learning methods that allow you to tap into data, and find useful data points that can help solve the business problems you’re looking to tackle. Data mining is the practice that helps you start to uncover trends and patterns in data, and is a great discipline to begin with, whether it’s using Excel and a little bit of data or tapping into RapidMiner and starting to dive into Hadoop, Data mining spans the gambit on complexity and data quantities. Remember the first three points to keep the right context and not go overboard too soon though, and you’ll be in good shape.

Regardless of your career, there is a way data can no doubt help you professionally and impress your co workers and higher ups in the process. Start with the fundamentals, help answer important questions, and simply build from there and you’ll be a bonafide data analyst before you know it.

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