Last modified: July 11, 2019

Five Pillars: Availability

What is data availability?

Data is the fuel that drives any business organization. Whether it is about resource consumption, investments, competitor performance, industry trends and indicators, customer needs, market analysis, sales drivers, performance check, or workforce management, their payroll mechanisms, bonuses, over times, time checks and productivity, data plays a crucial role in determining the efficiency of these essential functions and processes.

For any organization to make the most out of data, it needs to be looked at holistically (capturing an exhaustive set of metrics by integrating multiple data sources) . Also, for data to be truly considered high quality, the data must be readily accessible and locatable, for consumption by a user at a whim.

In a truly data driven organization, everyone:

  • Can get insights from their data
  • Is aligned around a single north-star metric
  • Can easily access the data they require

According to Forbes, “less than 0.5% of all data is ever analyzed and used.” Furthermore, “just a 10% increase in data accessibility will result in $65 million additional income for a typical Fortune 1000 company.”

How do we make data available?

The data availability process

Data can be collected and made readily available by using the following steps:

  1. Data collection goals: The key question to ask before collecting any data is: “What problems are we trying to solve by collecting this data?”
  2. Identify issues/opportunities: Choose a priority issue / opportunity from the previous step for collecting data and then set the goals/objectives. Also, the specific goal for each issue may depend on a hypothesis about a specific problem. A sample hypothesis may be “ In a retail store, ‘Buy 1 get 1 free’_promotion runs better than _‘Flat 25% off’ “.
  3. Plan an approach: Organizations need to make decisions about…
    1. Who will be surveyed?
    2. How data will be collected?
    3. The sources of data that will be used, and
    4. Duration of the data collection project In the Step 2 example, the hypothesis can be tested by collecting and analyzing store sales transactions as well as promotions data.
  4. Collect data: Implementing a data collection plan requires attention to matters such as:
    1. Identifying the logistics, technology, resources and people.
    2. Using carefully controlled procedures for collecting, storing and accessing data for safeguarding privacy and personal information.
  5. Analyze/Interpret data: First, a sanity check (check for missing values, data type mismatches, duplicate records, outliers etc.) is performed on the data. Then, it can be analyzed using complex algorithms, or less so, depending on the methods used and the amount of data collected.


Data is growing across all areas of an enterprise, at the rate of 1.5 to 2.5 times a year. This needs to be captured and stored from computer portals, customer interactions, purchase records, websites, and an almost limitless variety of other sources. Ensuring availability of this vast amounts of data is paramount to make informed data-driven business decisions. The success of the data-driven approach is contingent upon the volume and quality of the data gathered.

Written by: Rohan Joseph
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