data availability vs data accessibility

Data Availability vs. Data Accessibility: Why They’re Not the Same

Data is expanding; over 2.5 quintillion bytes of information data is created daily. Approximately 90% of the world’s data was created in the past two years. At this staggering rate, one can only imagine the pace at which data will continue to grow and accumulate in the coming years. 

There are many ways to generate this data. The main contributors are smartphones, websites, computers, IoT devices, sensors, and much more. Anything that plugs in creates data. 

Data is an entity on its own; more often than not, it is hidden away in different portals, servers, and other data containers. Sometimes, incorrectly placed columns, incorrectly categorized elements, and obscure formats can skew data. Alternatively, information isn’t as readily available as one would like it to be. 

There are firewalls, security measures, and other protective layers, making data accessibility an unending nightmare. There are ways and means which act as workarounds to access these data sources, but they come at a price. Add lack of knowledge to this list, and you will begin your journey towards utter bewilderment. 

Let’s stop going around in circles and address the metaphorical “white elephant” in this article. We have data, but can we use it readily? Let’s find out. 

Data Availability 

Different organizations rely on different types of data to keep their functions alive and running throughout the day. The term data availability means your employees should be able to use data as and when they need it. 

Data availability is having the capability to view, transform and move data around when needed. However, this might not always be the case. Data is a dynamic entity and is influenced by many different factors, some of which are not always controllable. 

Causes of Data Unavailability

An organization’s IT team plays an integral role in maintaining the sanctity of the data availability cycle. Despite their intervention, even the IT team can’t do much when it comes to addressing the following challenges:

  1. Host server failure: If the primary storage server fails, data will be unavailable till the outage is fixed.
  2. Storage failures: Most times, organizations tend to use on-prem servers or third-party servers for data storage. Either way, outages are a common problem in both scenarios.  
  3. Data quality: Poor quality data impacts data availability directly. Data needs to be relevant and updated to make sound business decisions.

Data Accessibility

Data availability is one thing, but data accessibility and transparency are other significant challenges facing organizations. Today, most business data is siloed and hidden away within walled containers. 

Download our free Silobreakers e-book to learn how to knock down silos and give analysts the power to choose their data sources, define their own calculations, and conduct data analyses using the tools they prefer.

Easily accessible data enables businesses to make quick decisions, focus on their products, and build a data-driven culture. 74% of companies said they want to be data-driven; however, only 29% of these businesses said they could connect data to analytics. 

If inaccessible data hinders strategic decisions, you can’t call your organization data-driven. Today, there are hundreds of tools in the market, each of which manipulates data in different ways. More often than not, the level of data accessibility and level of analysis provided by such tools is inversely proportional to the other. 

Tools like SQL and Python can connect to different databases and pull data into a more easy-to-understand format. On the other hand, what happens to people who don’t know such tools, and thereby, can’t access relevant data from data warehouses, data lakes, and more? 

Power users are not the only people who need to access day-to-day data. Front-line staff, interns, mid-level managers, and others also need access to this data. Data access solutions should be built to cater to everyone’s needs and make information readily available for non-technical users. 

Empowering Your Business Users to Access, Prepare, and Act on Your Data

Data is mutable, and there are tons of processes that transform it into an optimized schema — making data available and accessible for end-users. Subsequently, given the overwhelming and unfathomable amount of data generated daily, there’s no way to keep track of every piece of information stored in data vaults and other server repositories. 

Without automation, your data science teams will plateau, and your organization’s aim to be a data-driven organization will go for a toss. To achieve success and use data effectively, you need to offload your data and analysis requirements to various teams, like data engineering, data science, and data analytics. 

Using Tools to Make Effective Data-Backed Decisions Data-Driven

Data-driven teams have their challenges to deal with. As more and more teams begin to rely on data analytics for decision-making, there is a need to bridge the gap between available and accessible data. 

Many organizations tend to look for opportunities to upscale their existing set of employees and make the most out of the available data. Data needs to be accessible; however, they often fall short and make assumptions instead of data-influenced decisions.

Bridging the Gap With Kloudio’s Suite of Product-Based Offerings

Kloudio can help bridge the gap between data availability and data accessibility.  Our platform is aimed at extracting, transforming, and intelligently deriving business insights from different data sources, in a matter of minutes. Connect data from any database or SaaS application to your preferred spreadsheet without needing daunting lines of code or complex SQL queries.

Our goal is to make data availability and data accessibility synonymous. Create your free Kloudio account today.

Share this post

Share on facebook
Share on google
Share on twitter
Share on linkedin
Share on pinterest
Share on print
Share on email

Kloudio empowers you to do more with your data

Recent Posts

reporting analysis

Data Reporting vs. Analysis

Establish a difference between data reporting and data analysis with the respective tools for each segment.
All articles loaded
No more articles to load
Scroll to Top