Don’t focus on the data, focus on what the data tells you

Should we stop talking about big data, and start talking about insights?In a recent post on O’Reilly Radar titled “Turning big data into actionable insights“, Ben Lorica provides some highlights of an interview with Evangelos Simoudis regarding big data.

One of the highlights is extremely important. It relates to the importance of finding actionable insights from big data and sharing those insights in a way that the organization can use.  The quote is:

I think you need three ingredients. You need data, you need the right ways to combine the data and extract features from that data, and then the third ingredient is the ability to analyze the data and bring together the analysis results in a way that provides these insights and these measurable actions. … [They] need to be able to know what actions [they] need to execute in response to these analytics….

Emphasis mine.

So many times when talking about big data, we get ourselves wrapped up in the technologies, processes and systems that we forget to think about the real reason we are even working with data in the first place.  Data is near worthless until it is analyzed.  Sure, you can put some ‘value’ on data but unless you turn that data into information (and then into knowledge) your doing nothing more than being a data hoarder.

So…to my data hoarding friends I say: Don’t focus on the data, focus on what the data tells you.  I don’t care if you have 1 GB of data or 1 PB of data, if you can’t turn that data into information and then knowledge, your data initiatives aren’t going to succeed.  Additionally, if you can’t communicate the insights gained from your analysis, your missing out on the real value in data analysis.

Start small with Big Data

Start small with big dataBig data doesn’t have to be used to solve big problems. Big data sure makes it easier to solve big problems, but you can just as easily use big data and data analytics to solve ‘smaller’ issues.

In fact, if your organization is just getting started in the world of big data, it makes sense to find a few smaller problems to try to solve.  These small problems allow you to tweak your systems and processes to make sure you are gathering, storing and analyzing data correctly. These small problems also let you build up the appropriate skills within your teams to ensure when the big problems come along, your teams are ready to handle them.

Wouldn’t you feel better about your big data initiatives if you could ‘prove’ that the systems, processes and people were working effectively and giving output that can be believed? Most people would…and that’s why most organizations should start with these small projects.  There’s nothing worse than getting 6 months (or a year) down the big data road and realize your data has been collected and stored in a way that makes it difficult to believe whether that data is correct and ‘clean’.

As important as making sure your systems, processes and people are working effectively is, it is just as important to make sure your organization is ready to accept the outcomes of any big data analysis.  There’s nothing worse than spending time and money and realizing that your organization (or certain people within your organization) aren’t willing or able to accept the outcome of work performed analyzing the company’s data.

Starting small with big data lets you and your organization get comfortable with the entire process of collecting, storing, analyzing and reporting.  Big data doesn’t have to require big projects, big budgets or big teams…especially when starting out.

Best practices aren’t enough

Trading Psychology 2.0: From Best Practices to Best ProcessesI’m currently reading Brett Steenbarger’s “Trading Psychology 2.0: From Best Practices to Best Processes.” I’m a fan of Dr. Steenbarger’s work and bought his new book as soon as it was released.

While this book is targeted at traders and investors, there’s a great deal of knowledge that can be gleaned from it that can be applied to many other aspects of your personal life as well as business.

For example, the premise of the book is that the four main ‘habits’ a trader should focus on to improve their trading are “adaptability, creativity, productivity, and self-management.”   Those four habits are ideal of any individual and any company to focus on as well.   Just like a trader, a business can have the best strategy in the world but if they can’t execute on that strategy and adapt to changing markets in a creative way, the strategy is (or will become) worthless.

I ran across this nugget in one of the first chapters and thought it worth sharing:

…what makes traders good are best practices—sound methods for deploying capital and managing risk. What makes traders great are best processes: detailed routines that turn best practices into consistent habits. Adaptability, creativity, productivity, and self-management: These aren’t just things that the best traders have. They are what best traders do—routinely.

Emphasis mine.

Interesting concept isn’t it?  Best practices aren’t enough….to be consistent and remain in business as a trader, consultant, person or company, you need best practices and best processes.  But the key to long-term success in anything is how you build, change and manage your best practices and best processes.

A best practice today isn’t going to be a best practice next year.  The same is true for processes. Something that works today may not necessarily work next year.   You need both best practices and best processes but you also need to have the wisdom to know when those need to change to adapt to the changing marketplace.

This is where the four key habits come into play.  If you can adapt your strategy you can survive.  If you can think of new ways to approach your market and client base, you can survive.  If you can be both creative and adaptable while doing the right things and managing yourself and/or your business, you’ll not only survive but you should thrive.

Best practices aren’t enough. You’ve got to add best processes into the mix.  Additionally, you (or your business) need to have the smarts and creativity to know when its time to change those processes and practices to better align yourself and your strategy with existing or future market conditions.

Back to the book…while it is targeted at trading, there are a lot of very valuable insights that can be applied to all aspects of your life…I highly recommend it.

…and then what?

...and then what?I just finished reading a great article titled “The Most Important Question You Can Ask: Then What?

In the article, the author writes:

The great art of life is in balancing the short term and the long term, so that one can have enjoyment with integrity – pleasure with purpose. But in most areas of life, we pay strict attention to the immediate consequences of things. We look at the immediate results of a social or economic policy and call it a victory (or a complete failure).

The solution to  ‘short term’ thinking, according to the author, is to ask “…and then what?”.  By asking this simple question, we can force ourselves to look past the immediate and into the longer term. The author writes:

The problem is that so few of us take the effort to do this very simple thing. It’s understandable, we get caught up in the moment, and we don’t particularly enjoy thinking in minute detail each and every moment of our lives. But in the coming era, it will become increasingly important for us to ask these kinds of things, as our interconnectedness makes ideas and new technologies spread faster than ever before.

This very simple step of asking “…and then what?” can make a huge difference to any individual’s or organization’s planning process. By thinking about the step after the step, you’ll be able to open up plans to include much more than just the things needed to get the current project complete.

A perfect example of the lack of asking “…and then what?” can be found with most instances of the phenomenon known as Shadow IT.  Shadow IT usually arises because the IT organization can’t/won’t give a person/group a technology/system that they think they need. This group then goes out and finds something to fill their immediate need without thinking ahead. What will happen when the data in that new system needs to be integrated with other company systems, needs to be backed up or you need to move it to another cloud service provider? These are all very simple scenarios that can be covered if you simply ask “…and then what?”

Are you and your organization asking yourselves “…and then what?” during your planning?

Are you looking at all aspects of your business to identify cost savings?

Savings AheadI just read “Businesses prefer to cut in-house costs than challenge providers of outsourced services, survey finds” and just had to write a bit about the article.

The article, which reports on a survey by Alsbridge, claims that many businesses today are focused on cutting internal costs rather than looking at outsourced providers and services for cost savings.

According to the article, “managers are reluctant to question service providers because the multi-vendor ecosystems they have built are too delicate to tamper with and too big to fail. Instead, businesses looking to cut costs are focusing ‘excessively’ on in-house labour costs.”

Seems a bit strange doesn’t it…only looking internally for cost savings?  If you are looking to cut costs, wouldn’t you want to take a look at your entire operation to look for efficiency gains and places for savings?

According to the article, the reason companies aren’t able or willing to look at outside vendors is due to the complexity of the services those vendors provide and the intermingling between multiple vendor’s services and systems.  I realize modern organization’s are complex and the technology infrastructure to support these organizations is also complex, but complexity shouldn’t be a reason for not doing something.

I can’t think of a single argument that any CIO or IT leader (or any business leader) could make that would convince me that they shouldn’t look at every single aspect of their operation to identify places for cost savings.  I can’t imagine any level of complexity that would keep me from looking at every aspect of the business for places to save money.

What about you and your organization? Are you looking at all aspects of your business to identify cost savings?

Losing Big with Big Data

big_data1I help companies use big data to work better.  I love what I do and I love seeing companies and people succeed with big data.

That said, I’ve seen my fair share of companies (and people) lose with big data too. Most of these ‘losses’ aren’t due to bad data or poor usage of analytical tools. Most of these losses can be traced to a few simple decisions that were made when a big data initiative was being planned.

There are plenty of things that can go wrong during a big data ‘project’ but most of the real problems that can cause large problems have to do with the ‘strategic’ side of big data projects.  Tactical problems can be overcome but it is much harder to overcome poor planning and strategy.

From a strategic standpoint, there are quite a few questions that need to be answered when planning for big data. A few of the big ones are:

  • Do you have the right people?  Big data requires different people than other data analysis projects.   Big data isn’t data warehousing. With big data organizations should look for ways to get the data and the analysis of that data into the hands of the people closest to the problems trying to be solved.  With that being the case,  you need to have people throughout the organization that are curious, interested in analyzing data and willing to learn. Additionally, you’ll need IT professionals with the same skills and interests.
  • Is your project too ‘big’? Big data can bring big changes to an organization but if you bite off more than you can chew, you may only be wasting time and money in your big data initiatives. There’s nothing wrong with starting small with big data….better to start small, learn and possibly fail then to jump into big data with a great deal of money and time invested and fail. Find projects that let you get some big data experience under your belt without spending a great deal of money. Once you’ve got some experience (and some wins), then start working on larger and larger projects
  • Are you willing to invest for the long term?  Big data isn’t something you put money in one time and hope to be successful.  You can’t just ‘pay once’ and be done. With big data, you’ll need to continue to pay for new systems, new technologies, new skills and new people. 
  • Are you willing and able to open up your data? Some of the most successful companies using big data that I’ve worked with (and heard about) are ones that have opened up their data to their organization. This doesn’t mean that you should allow everyone unfettered access to all your data but it does mean finding ways to allow access to data with proper access rights and security.  Using proper data management and data governance systems and methods allows you to open up access to your data to anyone that needs access.   With open data access you’ll get more eyes on your data and more insight into ways to solve your problems.

I could probably continue with many more questions/issues that need to be addressed but these four are key to getting started on the right path in big data.

I’ve worked with a few companies who didn’t answer these questions before starting up big data initiatives. In some cases the failure was small and easily managed but in others the failure was quite large and expensive.  These organizations lost money, time and revenue from the failure of these projects.

Even more importantly (and perhaps more dangerously), they lost quite a bit of confidence in their ability to ‘do’ big data. They became very very concerned about planning for any future big data initiatives because they felt that it was ‘just too hard’.    But…it really isn’t all that hard.

You don’t have to lose big with big data.  Big data is complex and difficult, but with the proper planning and strategic thinking, you can prepare for many of the challenges that you’ll face in your big data initiatives.

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