Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Social Media: Back to Spreadsheets

It’s a dirty word right now – spreadsheets.  IT departments want to remove our dependence on spreadsheets and convert us over to a secure, controlled, shared, and robust analytic environment.  I would love that!  But, I have a problem, social media.

I’m managing more properties and content that is outside the realm of my corporate environment but I still have to report back and show how it is doing.  The only way I can do this is by using several analytic tools across multiple properties.  I grab the stats I need and punch that into a spreadsheet.  Then, I go to my web analytics reports, grab those stats, and consolidate them with my social media data on my spreadsheet. After that, I consolidate my lead metrics with my internet metrics for a 360° view of my marketing efforts.

It is all very time consuming and open to data entry error.

Business Intelligence is great to track internal process, but it is doing nothing to help track activities outside the corporate environment.  So, I’m stuck with spreadsheets.  Can you help?

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Archiving Strategy: Data Relevance

We often think of the relavence of data when we want to include or exclude it from analysis or process.  However, are you thinking about relavence as part of your data quality effort?

Just as you focus data quality efforts to clean existing information, there are invariably records that can’t be cleansed or enhanced.  They have no value in either business analytics or business process.  They are noise, similar to the noise you have when there is bad data.  To save and maintain them in your database can affect your ability to accurately analyze information, continue to deflate confidence in data, and if a significant percentage of your database, will cause problems in performance and added maintenance.  Developing an archival strategy as part of your data quality practice is a significant component that should not be overlooked.

Benefits of Data Relevance

  • Trust in data
  • Enables process
  • Accuracy of analysis
  • Supports decisions
  • Database optimization

It can be tempting to simply delete records from your databases.  Though, this can have a detrimental affect due to data dependencies within your databases as well as causing non-compliance in regulated environments.  Instead, it is best to formulate a strategy that flags non-relevant data removing or suppressing it from user interfaces and analytics.

Components of Archiving Strategy

  • Data decay rates – Attributes of records that loose relevance over time.  This component is a good guide on the frequency at which you will focus cleansing efforts.  It also provides an indicator on when data is approaching a horizon when a record will lose its relevance.  Age of the data and activity related to a record, even if a record is complete, can signify whether the data is relavant and open to archiving.
  • Minimum requirements of record viability – Records should continually be assessed to determine if they meet the minimum standards of use.  Failure to meet minimum requirements is a leading indicator that the record is a candidate for archiving.
  • Relevance of record to analysis, process, decisions – If a record is not going to be used in analysis, process, or decision making, there is not need to keep it in use.  This may be the case if processes have been optimized and certain information is no longer needed.  Or, it could be that it was a candidate for archiving due to decay rates and minimum data requirements.  Additionally, relavance may be determined when integrating systems where old records with old transaction history is not relevant to the existing or new business.
  • Regulatory compliance – In highly regulated environments like health care, there are standards on what you can and cannot remove.  Records may not be useful in existing process, analysis, and decision making, but might be required in certification or other compliance related activities.  Archiving ensures that information is not deleted from primary systems.  Although, you may have to provide a mechanism that provides adequate access to data for compliance.

An archiving strategy is a critical component of data quality best practices.  It will continually help you focus on improving and refining your data quality projects as well as thinking strategically about how you use and manage your data on a daily basis.  Establish an archiving strategy at the forefront of your data quality initiatives and you start your efforts off on the right foot.

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Business Intelligence: Decisions, Decisions

Business Intelligence is all about supporting business decision.”

How many times have you heard that?  It’s become the standard mantra.  It is so ubiquitous that I don’t think anyone questions anymore the validity of the statement.  It just is.  However, this is probably the hardest part to facilitate when building out you business intelligence practice.  Facilitating decisions is what makes BI stragetic.

Just what is the business decision? What does a business decision look like?

Elements of a Business Decision:

  • Purpose:  drive a business outcome – ex: revenue, shareholder value, profitability, market share
  • Position:  leads a company, division, department
  • Point in Time:  transition along a process or environment

A typical approach during the business analysis phase for BI is to at business decisions across a business process and where questions are asked to change behavior in that process.  Although, the difficulty with this level of granularity is that it is too deep.  These transition points are tactical.  Intelligence across this process and at these decision points is important, but you don’t get the strategic value of BI at this level.  You need to look at the outcome of the process and provide a platform that supports the decision of what to do next.  This is the unstated question.

Let’s take an example.  Sales management will always want a perspective on the pipeline and forecast.  This shows them how they are meeting their numbers quarter to quarter.  However, outside of conversion and volume, there are business decisions that sales managers need to make.  Should they adjust their territories to capture new opportunity or shore up existing business?  Are there changes needed in commissions to incent sales people along certain products and services to improve profitability or revenue?   BI can lead sales management with insights that will guide them to optimize their processes and management rather than just data.

Purpose:  market share, revenue, profit
Position:  sales
Point in Time:  aligned to quarterly pipeline and forecast

To align BI to the business decision it is important to include executives in the discussion.  Get beyond the reports they want to see and ask the question about how they manage their business.  Walk through scenarios of what they ask as changes in the market or the business arise and how information can help them make a decision.  The better able you are to see how they manage their business, the more valuable the BI practice will be to supporting the business.

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Stuck in First Gear

porscheBig investments were made in recent years in IT.  IBM, Oracle/Siebel and SAP lead the market and were successful not only with the multi-national enterprise companies but, also with mid-sized companies.  There are a lot of companies out there that have purchased application and data management/data warehouse solutions only to find themselves using a portion of what it could do.  It’s like driving a Porcshe in first gear.

There are some fundamental reasons for this, outside of the fact that companies may feel it is the fault of their sales execute selling them the wrong bill of goods.  IT will blame the business for not knowing what it wants.  The business will blame IT for not getting it.  Doesn’t really matter, there is plenty of blame to go around.  What matters is that now you have a solution that isn’t giving you the benefits that it really could and should be.

Maybe I’m a bit biased since I’m the data chick.  Well, more than a bit.  Regardless, I think that from a data management perspective, companies are failing.  The maniacal focus on process efficiency has drowned out the fact that process runs on data and feeds data.  This focus has put data in the back seat too long and now when we need it to better understand our customers, our business, and make decisions, it is sorely lacking.  Our data lacks unity, structure, definition, and most of all purpose.  Companies simply cannot leverage their information except at very basic levels.  When things are good, this may be okay.  When things are bad, this is a real problem.

What makes this even more sad, is that companies are looking to spend more money on applications and data infrastructure to ‘fix’ the problem.  The promise of the new model and more sophisticated bells and whistles that will solve anything you throw at it is just marketing.  Until you can understand and control what you already have under your hood, getting something bigger, better, and shinier isn’t going to help anymore than it does now.  So, there was no ROI on existing purchases and there won’t be any ROI on new purchases.

There are two things companies need to do to make the investments in enterprise solutions worthwhile:

  • Clean-up the back-end data management practice so that it is fluid with business process and application usage.
  • Have a clear data management strategy for new applications that is fluid and scalable outside of application databases.

Your company may already be embarking on SOA or MDM projects.  But, have you looked at how these new practices will support applications outside of changing the oil?  Can the data drive process?

Today, applications are bogged down because data is treated as something to put in the trunk and horde.  Until data is thought of as fuel, you’re IT investments will stay in 1st gear and never get to 6th.  Now how fun is that?

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Starting Your Business: Data From the Ground Up

data managementIt is easy when starting up a business to think about selling first, marketing and database management later.  Afterall, revenue is the most important thing to focus on.  Though, once you get over the hump and begin to groove, you realize that data is important.  Now you have to sort through it and it feels worse than diving into list of 300 emails in your daily inbox.  Well, if you have a method to deal with your email inbox, create one for managing customer and contact data.

Here are some simple things you can do up front to stay organized and be better prepared when you are ready to look at and manage your customers and the business in depth.

  • Be consistent about how you collect customer data – There are usually several layers to the importance of customer information elements depending on your relationship.  What you want to do is determine the information that is most critical and collect this consistently across all methods.  Keep in mind that what is mandatory to transaction may be different from what you need to follow-up with customers after a purchase.  So, make sure that you take this into account at the point in time you collect the information.  It is harder and more costly to collect after the fact.
  • Save data elements into dedicated fields – The biggest issue I find with new businesses and small businesses when they need to convert to more robust systems is that data elements are merged together into a single Excel cell.  When collecting contact names, break apart the first and last name into separate fields.   Do the same for addresses having fields for street address, city, state, country, and postal code.
  • Determine what platform has the Master data – The second biggest issue when migrating customers to a robust system is the inability to determine which record is the most valid of duplicate entries.  If you are saving contact and company information between your mobile phone, laptop, website, and company server, which will you consider the single source of record?  Once you determine this, make sure you sync your lists to that source.  I recommend you do this weekly at the least and use your primary server.  Then, include the database in a weekly back-up process.
  • Save, Save, Save – You may have caught this recommendation in the previous bullet.  Backing up is critical.  It is mandatory.  I’ve watch small businesses loose business critical information because they didn’t back up or back up often enough.  There are easy services today that make backing up our information simple.  At the very least, invest in a USB storage device and plug into daily when you sit down and get to work.  Before you do anything, back up.  Make it a habit.

Managing your customer and company information does not have to be difficult or cumbersome.  With a little forethought, when you business gets off the ground and you are ready to invest in better platforms and reporting, you will have a great foundation to do so.

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Cool Data Visualization – What is That?

Want to help your organization optimize operations, extract market opportunity, see what customers think?  Provide a visual representation in a single slide that tells your senior executives what is happening and what to do.  That is, if they can understand it in a milli-second.

There are a lot of really great algorithms that are creating interesting visual presentations of behavior, influence, and connections across people and topics.  The problem is you might as well be looking at fractals for all the business insight you gain.  It may provide perspective for the resident math geek, but for the average business executive it is just modern art that needs further interpretation.

I remember in college when I first started programming mathematical equations to model data and played with fractals.  It was exciting, creative, and helped me to link data with a tangible result versus a simplified equation or answer.  I used to put fractals up on my website like works of art.  I even included a link to input random numbers for others to create their own.  It was so cool!  Was it practical?

Don’t get me wrong, I love data visualization obviously.  It can simplify very complex analysis to gain insight faster.  What I’m struggling with is needing the ability to connect data visualization with executive intuitiveness.  Heat maps, network graphs, and the variety of data maps I see being generated today are a far cry from what I would bring into a budget meeting let alone show to board members.  More time is spent explaining what executives are looking at than having conversations about business objectives and investments.  It is also not just executives.  Business managers and directors need to understand the business as well.  Pretty is nice, but value is better.

The other aspect that of today’s data visualization leaps is that it disassociates the business from the information.  If only a small group of geeky mathematicians and programmers understand the data, it creates a mysticism that can lead to distrust of information.  If people don’t understand it, they don’t learn, and they don’t improve.

What I hope we can do as really smart statisticians, data analysts, and programmers is make the connection between information and visualization so that it further democratizes insight and empowers our business rather than mystify.

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