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8 Data Collection Mistakes Your Company Needs to Avoid

“Basically, our goal is to organize the world’s information and to make it universally accessible and useful,” said Larry Page.  These pithy words by the founder of Google capture the power and benefits of data.  Data can provide immense value, insight and financial return if collected, organized and digested properly.  Sadly I’ve seen many shortsighted attempts at data collection by organizations of various sizes.  Here are eight data collection mistakes that I’ve come across that can hinder the return on investment.

 
1. Not Enough DataOne of the most common mistakes in data collection is failing to   collect any data.  Many organizations and small businesses in particular fail to realize the value of data collection until an urgent need arises.  For example, a local retail store may want to introduce a new product to the community.  If that retail store does not collect any data about their customers and their purchasing patterns the success of that product is simply a hopeful endeavor.  It is just like the "If we build it they will come" adage.  History has reminded us far too often that "we may build it but they have not promised to come".  A Customer purchasing analysis must take place to determine if this new product is a good fit.  Every small business should take a proactive approach to collecting data in order to influence decisions about new products and markets.  When hunting for data to collect, follow an acronym model we like to call D.L.D.  This stands for Depth, Length and Diversity.  Depth involves collecting data at the most basic level possible, it’s really about granularity. (By person, client, and product).  Length involves collecting the same data at the same depth for a prolonged period of time.  This should be over years.  This type of data to must have depth.  When does the customer typically purchase? What month? Time of month? Day of week? Hour of day? The final component is diversity.  Data collection should not be restricted to financial transactions.  Collect an array of data and utilize the plethora of free data available to the benefit of your organization.

2.
 Wrong Data – Akin to skimping on the type of data collected is to collect the wrong data.  Due to budget constraints many institutions do not have the bandwidth to handle the volume, velocity, variety and veracity of this data saturated world.   Firms need to make decisions on which data is the most profitable to track.

 

3.  Dismissing Unstructured External Data – Another peril in the data collection process has to do with data formats.  In today’s world data is presented in a variety of formats.  Data can be carried in multimedia formats and text.  They can appear in structured formats which can easily fit into rows and columns or can be unstructured.  Examples of unstructured data would be emails from customers, feedback on blogs and information in photos.  Useful data is available everywhere.  Companies should pursue techniques to gather and interpret these data.  For example, many websites contain a lot of helpful information.  A VBA (visual basic for applications) program can be developed in Excel to search the website and collected helpful bits of information.  Some examples of information can be competitor prices, local demographic data or company catalogs to name a few.   Find ways to get data into usable formats for analysis.

 

 4.  Misunderstanding Intent of Data Collection –Another pitfall in the data collection process has to do with not understanding the “end game”.  By that, I mean the purpose of that collection.  Does Senior Leadership need to be informed about competition?  Is the purpose to benchmark against the industry?  Is it to test the possible return of a new product?  The answer to each of these questions will dictate different data collection approaches.  An organization that doesn’t understand the purpose or intent of data collection is a stagnate one.  These types of organizations are reactive rather than progressive.  In short, the intent is influenced by Senior Leadership and the environment. 

 

5. Data Analysis Method Ignorance – It is helpful to understand the data analysis methods being used.  That knowledge will guide the design and focus of data collection strategies.  Will VBA be used?  Will it be a manual process? Where is the source of data?  These seemingly small questions can impact the end product.


6. Financial Equivalent –Finances are one element of data collection that is consistently missed.  Every data collection process should be accompanied with ways to quantify the financial impact.  At the end of the day, every organization wants to use the data that it has available to increase profit.  Data should be collected with the intent to paint a financial picture down the road.  For example, a local Baskin and Robbins has over 1,000 flavors to offer customers.  This type of business should not simply record the sale that was made, but the sale price of each transaction along with customer information.  This can help paint true story about the financials of the company.

 

7. Data Collection Systems & Controls – While more data in most cases is helpful, no organization can afford to track everything, every minute.  Rather than just giving up on data collection, survey the data currently collected and make an inventory.  Many organizations will be surprised at how much data is actually being collected in various systems.  For example data is available in QuickBooks, Sales Force, PeopleSoft and Excel spreadsheets to name a few.  After an inventory is made, brainstorm additional data which the company might find helpful.  After this brainstorm session done select the one that would yield the most benefit in light of the data already gathered.  Finally focus resources into collecting those data.

 

8. Forgetting About the Key – Key is a term used in the software world to describe a special code that unlocks or connects different data tables or programs, to name a few.  One of the most important principles of data collection is to combine data from different sources.  For example, taking internal company data and adding industry or census data.  Keep in mind, the only way to combine different data sources is by using some type of key.  A key in that case is something that is similar in both data sets.  It can be time, customer profiles or location, to name a few.  At its core it is something that can be used to merge the two data sets.

 

The following thoughts are, but a few data collection mistakes made by companies of all sizes.  Taking these points into consideration will help organizations reap benefits from this data driven world.

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