Managing Data

Managing Data Application

This is a summary of some techniques for managing data so that it can be used effectively.  By using these techniques or practices you may find insight into your business that can push your business into a brand new market, or send profits rising beyond your expectations.  Before starting, let us get the language or vocabulary out of the way.

Data are the facts and figures that are collected, analyzed, and encapsulated so they can be interpreted.  A data set is the data collected for a purpose.  We can break the data down into elements.  Elements are the entity on which the data is collected.  For example, you have just collected the price of guitar picks from ten manufacturers of guitar picks.  The data set is ten elements.

A variable is a characteristic that may change for each element.  In the example of the guitar pick manufacturer, the price from each manufacturer is likely different. Manufacture 1 may be 11¢ and  Manufacture 2 maybe 12¢.  Again – the price, in this case, is the variable.

Data collected by measuring the variable on each element will result is a set of measurement called the observation.  In our example, manufacture one is selling for 11¢.  The cost of the pick (11¢) is the observation.

Now that we can come to an understanding of the linguistics of it all, you might have come to the conclusion that you’ve got some important data floating around at your company right now.  The problem is that you probably have a lot of important data in a lot of different places.  Perhaps in the office and out of the office.  If you’re lacking in the data management area, it will be difficult to interpret your data.  By following a few basic practices you can get to that data and take a better look at it.

Standardize Data Entry

Data comes from many sources these days.  Data often sits in different systems and in different formats, making it difficult to come to any valuable conclusion. Therefore, it’s important that you standardize all data entry formats and requirements to ensure fields are complete and formats are consistent. Some things as simple as a first and last name can cause issues in effectively reaching markets. Start by creating consistency in how data is entered into your systems. For example, use a uniform submission set on your registration forms, which will ensure data is correctly formatted when it enters your marketing system.

Maintain Data Continuously

Effective marketing data management requires ongoing maintenance—it’s not a one-and-done affair. Records left unattended can rapidly become stale and inaccurate, it is necessary to develop and deploy a maintenance program. Undeliverable emails, inaccurate postal addresses, and non-working phone numbers waste a considerable amount of marketing resources and degrade the overall performance of marketing campaigns. By cleansing and enriching data you will increase the number of inquiries and leads than if you did not.

The maintenance program should replace high-quality data into inaccurate or incorrect records, and remove contacts that aren’t your target buyers or don’t influence your marketing goals. It is not that difficult and a process that can increasingly be automated for you by a data management provider.

Data Management Plan

Strategic Goals
First and most importantly, check that the marketing campaign and selected KPIs (Key Performance Indicators) align with your overall marketing strategy (lead generation, nurture, content engagement, top of the funnel awareness) for the campaign. While it seems obvious – if this step is missed the entire plan will not be fruitful. You must find an alignment between the desired marketing objective, the campaign tactics and the rubrics that will be used to decide success and help optimize the plan.

Go with data, not your gut.  We like to go with our gut because we believe we can trust it, plus it is easier than trying to wrangle and tame large amounts of data. History shows, “bad idea.”  A data management plan aligned with a marketing strategy is a solid move toward a smart and seamless flow of useful interpretation of the elements and variables in your data.

Start off with an inventory of all of the first party data your company has. First party data includes:

• Social likes and shares
• Data from mobile devices or apps
• User subscription data
• CRM data
• Anything that can be broken down into variable

Data collected by surveys, email marketing, and other avenues – all just sitting there mostly unused – inventory. Once you have those elements nailed down, then you can see about getting other variables (information) to round out your elements (perhaps profiles).

You may be surprised to learn what other co-workers or departments have as data. It may be something you never knew about or never thought to use. Here we are consolidating between everyone’s crucial information so that it does not fall through the cracks.

Decide What Information You’re Going to Use

The data is all together now, so what’s relevant? Relevancy depends on your industry and what the end game is. You could be selling a physical product, be focused on audiences seeing your brand, or promotional coupons. All data needs to be prioritized and put into a plan. For example, a company sells umbrellas and would like to send out a promotion and target people in Texas.  This umbrella company heard the weather folks are predicting rains for the next two weeks in that state; they would pull all elements (people) that have a variable (the state) populated with the state of Texas.  The umbrella company put together a plan on how to use the data. Now the marketing team can send out an email campaign to the people of Texas or by any other means of communication.

Capture the Power of Virtualization Technology

More data generally means better predictors, so bigger often is better when it comes to how much data your business analysts and data experts can get their hands on. With access to more data, it’s easier to quickly determine which data will best predict an outcome. The problem is large amounts of data results in three basic challenges: storing, processing and managing it efficiently.

Businesses must virtualize their unique data set so that not only multiple applications can reuse the same data, but also the smaller collections of data can be stored on any vendor-independent storage device.  This will also prevent data from being duplicated or synthesized.

Now that the data is one location, businesses can dramatically improve data management in three key areas:

• Data can be better secured since the management is centralized, even though access is distributed.
• Results of data analysis are more accurate since all copies of data are visible.
• Less time is required by applications to process data.

Virtualize data management not only allows data to be backed up far more effectively but also makes it more easily recoverable and accessible with huge cost savings. Plus it frees up your IT staff to focus on other strategic technology initiatives that drive company growth instead of wasting time with an out-of-control data snake pit.

Effective Management of Data Results

By effectively using these techniques or practices, the data your businesses create become an invaluable resource. The last thing you want to do is spend time and resources collecting data and business intelligence, only to lose or misplace that information. This means you would then have to spend time and resources again to get that same business intelligence you already had.

The effective management of data within any businesses has grown in importance in recent years as businesses are subject to an increasing number of regulations, large increases in storage information and storage capacity, and the sheer amount of data and documents being generated by businesses. This rate of growth is not expected to slow down, so get ahead of it before it costs more than you can afford.

See: TOP PRINCIPLES OF MARKETING and look at Segmentation.  This post may give you ideas on how to organize your date.