Planning Analytics Blog

Your analytics thought partner

More on Data Visualization for Strategic Planning

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Data VisualizationThere are three main steps to completing a data visualization. Steps 1 and 2 are interchangeable and may sometimes occur simultaneously

  1. Formulating the question you want your visualization to answer or the story you want your visualization to tell
  2. Gathering, understanding, and sorting the data
  3. Applying the visual representation

Examining the Data

  • Do you have all the data you need?
  • Does it include all the variables that you are interested in?
  • Are there any obvious errors in your data?
  • Is there any data that is missing?

Understanding the Data Types

  • What type of data have you acquired?

Transforming for Quality

  • Do you need to clean up your data?
  • Do you need to fix any errors or fill in any gaps in your data?

Transforming for Analysis

  • Parsing (splitting up) and variables, such as extracting year from a date value
  • Merging variables to form new ones, such as creating a whole name out of title, forename, and surname
  • Converting qualitative data/free-text into coded values or keywords
  • Deriving new values out of others, such as gender from title or a sentiment out of some qualitative data
  • Creating calculations for use in analysis, such as percentage proportions
  • Removing redundant data for which you have no planned use (be careful though!)

Do you need more tools to get your data into a visual storytelling format? Take a look at the Stanford University tool; Or talk to us before you commit your valuable assets.

We can get you off to a hot, fast start in 2016. We’ll do the heavy lifting to transform and load your data into meaningful sets, mobilize and monitor your planning analytics so they enhance your BI priorities. We’ll give you insights into trends, measure progress toward a set of quantitative goals, flag shifts in your markets or financial operations and even lift your customer experiences to a higher level. Whew, that’s saying a lot. Or is that just what you needed to hear?

Call us to start a neutral and friendly conversation. 512 478 3848.

 

The Time to Plan For Your Big Data Needs is Today

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Big DataCompanies today want to use big data analytics as a way to discover trends to improve the way they conduct business and bring value to their stakeholders. That is a high-priority objective.

Finding correlations across multiple disparate data sources (48%), predicting customer behavior (46%) and predicting product or services sales (40%) are the three factors driving interest in Big Data analytics. These and other fascinating findings from InformationWeek’s 2015 Analytics & BI Survey provide a glimpse into how enterprises are selecting analytics applications and platforms. Source: Information Week 2015 Analytics & BI Survey.

In data analytics, time is of the essence. Business leaders and decision makers often require real-time or close to real-time information to make informed decisions. However, it is estimated that 80 percent of an analyst’s time is spent on preparing and cleaning data for analysis. This leaves only 20 percent for actual analysis work, and precious time is wasted on preparing the data for analysis.

So what can you do? Repurposing your top analysts to ramp up data transformation is not really productive in the short term. So don’t ask your I T team to do something they have little feel for and that drives down their overall productivity. Ask around. Find one or two external teams that have both the portfolio and the skills sets to review your scope of work and then jump in to deliver results (defined and meaningful data that drives good decisions) in less time, with less waste and with a lot less frustration.

Our ETL Plus* and custom application development expertise allow our analysts to mix and match data sets as needed. We can blend internal with external data sources to establish historical trends and project need and demand, which are the backbone of any feasibility study. Additionally, we can help you monitor trends, measure progress toward a quantitative goal or flag shifts in your market or business operations. Call us to start a conversation that can enhance your data management investment while keeping you ahead of your competition.

Data Analytics Require Careful Preparation

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Data Crystal BallBig data is growing bigger every day, but it’s still a very new trend for the business and IT worlds. Organizations are working to develop new ways of taking advantage of terabytes of available data, and in many cases it’s too early to determine which are the most effective methods. One aspect whose importance is often overlooked but that is a necessary part of any successful analytics project is data preparation.

Data can be hard to analyze if it is still raw and unpolished, especially when it comes from different sources, and certainly not all information that is available is necessarily useful. Attempting to soldier on without cleaning data up first will inevitably produce skewered and inaccurate results. In the field of computer science, the saying “garbage in, garbage out” reflects the inability of automated processes to separate the wheat from the chaff.

On any given analytics project, companies can use information generated specifically for that purpose or from automated sources. The data can come in any number of file formats or it may even be in physical files or come from outdated legacy systems. A comprehensive ETL architecture can comb all those sources, select only the relevant data and bring it together in a single destination, where it can be easily analyzed.

Management Information Analysis‘ ETL Plus* service allows for each part of the process to be customized to meet the specific requirements of each organization and project. We can transform vital information from very disparate sources and ensure that it is primed for analysis by eliminating duplicate and irrelevant data.