DanMaycock.com

Data, Strategy, Leadership, and Innovation

Category: Data Technology

Reviews / Discussion on data technologies, in several different categories (cloud, analytics, visualization, 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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3 Reasons Why You Need a Chief Analytics Officer

Data has exploded in a way that rivals mobile’s explosion ten years ago. Everyone is out there buying masters degrees, data visualization licenses, and data scientists by the truck loads in a way that mimics corporations buying mac laptops, mobile developers, and app store branding when iPhones blew up the smart phone space.

The Analytics ‘Trend’ Isn’t New

There are a lot of great things taking place right now with all the interest around data analysis, but the funny thing is that data analysis is nothing new (neither is data science). There’s a good 30-40 years of work on data, from data architecture to database administration (not to mention the millions of excel spreadsheets that corporations are running critical business functions on) that live inside companies and create a legacy layer that this latest wave of data analysis is building on.

Other new trends, such as big data analysis and the cloud computing revolution, have further spurred companies to consider ways to extract usefulness from their existing data and move away from churn or ARPU and develop distinctly competitive analysis with phrases like “regression analysis” and “predictive analytics” becoming much more common in corporate board rooms.

Translating Data

The big problem is, as was the case with mobile, is that you have to be able to translate interesting technology into impacting ROI-laden investments that drive top or bottom line revenues (or create efficiency and lower costs of course, as well). There’s a good deal of buzz around big data being an overused term, and a hundreds of millions of dollars spent on visualization tools will, at some point, taper off when the average business user turned dashboard builder runs out of things to visualize due to saturation, bad data, etc.

So Who / What Is This Chief Analytics Officer?

A Chief Analytics Officer could be a Director of Data, or a VP of Analytics, but having someone at an executive level that can drive a centralized data strategy for the company should exist for these three reasons.

  1. Centralizing Your Data Resources Will Help Avoid Silo’ed Capabilities

To turn all this hype into profit, it means building a centralized capacity. A capacity which sites outside of the IT-to-business politics and hype to buy visualization tools, and instead focusing on building a stack of capabilities, from the data lake to the dashboards, geared around revenue generating use cases taken from business partners who need more usefulness from their data without having to build silo’ed data science teams that rely on fractured data sets.

When anything is this pumped up, every department is going to want to get involved and build capabilities, since every business group uses data in some form or another. The problem is that it takes a variety of experiences and backgrounds, along with investments, that need to be built at a corporate level with a plan to centralize some capabilities and decentralize others with a clear data strategy that everyone can get behind.

Centralizing this capability means one strategy, one leader, and limitless opportunities for everyone to participate without each department deciding their own game plan for riding this data wave.

  1. Consolidating data to maximize usefulness, while aligning that effort under a single leader

The topics around big data, and data lakes are growing overwhelming, with more and more companies working to consolidate all their data in one place to allow for both advanced analytics & traditional business intelligence functions. At the same time, a data lake built in the wrong way can cause latency along with too many executive peers building extensive requirements which ultimately brings any progress to a halt.

Bringing your data consolidation effort under a single leader, tied to a data strategy that brings the bigger outcomes into focus and alignment while leaving the smaller day to day details up to a single org unit means your company can spend less time planning & debating, and more time driving value from your data lake.

  1. Impact is prioritized, over ‘interesting trends’

Much like the millions of dollars spent on corporate mobile apps that never got traction, companies today are spending millions of dollars on real time streaming, data visualization, and corporate education on DAX programming all in an attempt to capitalize on the data analytics hype and create a stronger bottom and/or top line revenue stream through the use of data analysis.

The thing is, data isn’t a new domain for technology, nor is investing in Big data going to revolutionize your company.

There’s a good deal of effort being spent on building impressive looking visuals, which add no incremental value over the same data displayed in an excel chart. Furthermore, companies investing in hiring legions of data scientists without clear revenue-driving hypothesis will find they spend a good deal of time figuring out just what to focus on.

As is the case with any over-hyped technology, whether it’s enterprise wide tableau licensing or infrastructure to support web traffic analysis for real time personalization, the tools are only as good as the capabilities on the team and the business cases they are actively working towards.

Focusing on a single leadership structure to come up with the real tangible value for investment in data analytics means there’s a common set of goals that’s driving the spend, and a clear idea of what each department and employee is focusing on.

It’s not so much that a single team owns every analyst, but rather each instrument is calibrated so the whole company sounds like a beautiful concerto vs a number of instruments playing at different rhythms.

Furthermore, when it comes to the vendor onslaught and procurement nightmares that naturally arise in the midst of a technology boom, there’s a clear investment strategy for how the company plans to leverage capabilities such as big data or advanced analytics. This can influence everything from recruiting and training, to infrastructure and software licensing, and help ensure each investment is additive vs expensive and lacking in impact.

There’s a good deal of interesting happenings in the data space right now, but companies need more impact to back up the cost.

There are no doubt other benefits I’ve missed out on taking data seriously, and putting someone in charge who is somewhat removed from the politics and inefficiencies that come from burying the capability inside an existing org (similar to the CIO coming of age, and now no longer reporting to CFOs in most companies).

The aim is however, to ensure your data analytics efforts are making a meaningful impact, and driving the kinds of returns most companies never experienced during the mobile app boom almost ten years ago now. And in so doing, benefiting every company that invests in the great capabilities a data-driven org has at its disposal.

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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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