Challenges Faced By Organizations In Handling SaaS Data

The main concern of every business is to manage their organizational data and keep the information safe to use it wisely. With advancements in technology and changing business trends, huge amounts of data are being generated. This raises questions like how to manage data so that it helps them grow their business and store historical data effectively to gain meaningful insights from the data, optimizing data productivity. Data lakes can address these challenges.

The Main Data Challenge: Velocity X Volume X Variety

What’s driving the increase in DataOps complexity? Although you can argue that data has always been on a growth trajectory, growth has exploded over the past few years. Couple that velocity with volume and variety, and you get the crux of today’s data challenge for companies. 


The speed at which we create data is a leading factor that requires DataOps to, at times, reimagine their approach. For example, a study by Forbes a few years ago found 2.5 quintillion bytes of data (about 2.5 million terabytes or 0.25 zettabytes) created each day. It is an impressive figure but, alone, it doesn’t tell the real velocity story. According to IDC, “the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage.” So, with this rapid increase, how are companies responding?


With that velocity comes volume. According to IDC, in 2020, 64.2ZB of data was created or replicated. So IT leaders and DataOps will have to decide what to keep, archive, and delete.  


If volume growth is a product of velocity, both are a product of variety. Over the past five years, we added new data types, think IoT, robotics, and chat logs, moved more data from on-premise to the cloud, and liberated more data through APIs. So today, companies have better access to more types of structured and unstructured data than ever before, what they decide to use, what they need to keep, and where will drive DataOps decisions.

How to Guard Against SaaS Data Loss

How to Guard Against SaaS Data Loss

SaaS Data As Fuel Of Your Business

Salesforce has been the center of fuel of almost every market. It helps thousands of businesses manage their marketing data, customers’ data and use them properly to gain new insights and opportunities to develop their business. Salesforce data give a base to the analytical abilities of the organization to draw deep insights and make better decisions. 

“Salesforce executive Keith Bigelow noted that at the rate of today’s doubling of modern data, there would be 44 trillion gigabytes of data available to businesses by 2020.”

However, companies are not able to use the power of data generated by Salesforce or any other data sources. As a result, it is found that most organizations are not able to use the information generated by them, and this unused and growing data generates lots of challenges for an organization.

Download Now – Brief – Manage your Salesforce data Effectively

Challenges in handling saas data

Getting Passed API Roadblocks 

APIs help liberate data, but they also pose challenges for DataOps, DevOps, and analytics platform ingestion. SaaS applications have proprietary web service APIs across SOAP, REST, or both. Properly managing them helps ensure that data-driven organizations and their solutions stay connected to the data that feed the data lakes that underpin analytics, AI, and ML initiatives. Two of the main challenges are: 

Poor Accessibility

Some SaaS applications do not provide a query interface, making it cumbersome to extract data. In many cases, each object is accessed with a different API, and each has unique rules for invoking, filtering, and searching.

Different Query Languages

SaaS applications have their syntax, which needs to be understood, making it difficult to access and manage data. For instance, you may use one query language to handle a particular API query, whereas that same query will not work with other APIs. The only option is to go directly through the application or develop custom code.

How do companies overcome these API challenges? Although DataOps and DevOps teams can go it alone, many find the economies and benefits of leveraging data management platforms. Why? Just as the cloud democratized access to many ERP systems, so are the SaaS data management platforms of today. As a result, DataOps and DevOps reduce costs and time by having these platforms do the heavy lifting, taking care of the ETL workloads, their accompanying scripts, and the ongoing API maintenance. 

Start Replicating Your Data Today

An overview of replicating SaaS applications like Salesforce and Dynamics 365.

An overview of replicating SaaS applications like Salesforce and Dynamics 365.


  • Break your SaaS data silos by extracting SaaS data through replication apps and loading it onto Data lakes or data warehouses. It eliminates many API issues. The results are regular updates for predictable and reliable data accessibility. It breaks data silos. SMB, mid-size and large enterprises can benefit from replicating data to external databases/data stores to easily draw insights from their data for analytics, AI, or ML initiatives.
  • When data is stored at one center and in different formats, it is easy to manage data as different tools can use it to analyze data easily and use it when required in the native state. Also, it eases up the accessibility of data for all the departments according to their need as all the information required by them could be found in one place.

Taking Stock of Your Dark Data 

Gartner defines dark data as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetizing).”

Dark data, like dark matter, exists and impacts a company but generally isn’t seen or used after its creation. Customer dark data includes chat or call logs and even geolocation data. As data’s three V’s (velocity, volume, and variety) have grown, so has dark data, and, like all data, it takes up storage space, has risks, and has value. Maximizing that value means getting the data into data lakes easily and quickly ingestible format by analytics, AI, and ML platforms.

Dark Data’s Value, the Opportunity Cost

More companies are tapping into dark data for their analytics. Why? Dark data holds the key to making better business decisions. Examples include better customer retention and product decisions based on customer chats, call logs, and geolocation data. However, loading that data into data lakes for further analysis has challenges that today’s data management platform can circumvent.

Dark Data Storage Costs 

According to Forrester, “On average, between 60% and 73% of all data within an enterprise goes unused for analytics.”

Although the time and effort to acquire, secure, and store data is significant, creating a mechanism to easily and quickly access the data for ingestion into analytics, AI, and ML platforms seems a bigger challenge. But the underlying cost of failing to use this information is massive. Furthermore, organizations’ unused data indicates that data is simply eating a lot of space in your applications. Thus, increasing the financial load of storage cost of the organization.


  • Either you see dark data as an opportunity or a reflection of problems, you cannot ignore its importance; the best way to handle dark data is to use it well. 
  • Moving data to public or private servers through replication technology can make it easy for everyone to access the data since today’s technology is preconfigured to automate the extraction and loading of data into the data lakes and warehouses.
  • When considering your space allocation provided by SaaS apps. The most economical solution can be archiving cold data or files from SaaS applications to lower-cost cloud data stores. But there is also the consideration of being able to recover all the needed data, files, and attachments in a reasonable amount of time, including the associated metadata. 

The ability to get more out of data is being made possible with data lakes that can ingest many types of data used by BI, AI, and ML platforms but only with the right extraction and translation tools. DevOps can create those tools for simple apps or DataOps to leverage 3rd-party tools to stay ahead of APIs and adjust to new sources and volumes of data. 

Impacts of poorly managed business data in SaaS applications

Poor access of data

When considering native SaaS extraction tools, consider the time needed to extract the data, the frequency of extraction, and other costs related to loading the data, such as updates to schemes; creating copies of large sets of SaaS application data is difficult without the right tools. In addition, some SaaS applications do not provide a query interface, making it cumbersome to extract data.

Storage cost

Many B2B SaaS applications provide a limited amount of storage and require you to add on in blocks similar to mobile providers. It gets expensive. Keeping unused data and other files can also consume space quickly in your saas application, and if you decide to acquire more space, you will most likely be buying it in blocks. This can cost you, for a CRM, as much as $1,500 per year for one additional block of 500MB of space.

Wrong Business strategies 

The primary role of business data is to allow you to make better decisions so that your plans have a higher chance of succeeding. When working with inaccurate data, business leaders and managers will only make the wrong conclusions. Instead of using data to get closer to their goal, they will do the opposite.

What will you lose by poor data management?

When considering native SaaS data storage, consider the time needed to extract the data, the frequency of extraction, and other costs related to loading the data, such as updates to schemas. Also, consider the data that cannot be extracted and the impact of recreating that metadata. Then compare this to the replication tools on the markets for a cost-benefit analysis. Some of these savings are a database administrator’s time which can cost $25K per year.

Benefits of Good Data Management

Ability to run complex queries

Replicating your saas data to a data lake or data warehouse gives the ability to run complex queries to extract the required information and get a deep insight into the data for better business opportunities. 

Integrated products to do business process integration

Replicating your saas application allows you to integrate your data and implement different applications to make your business process more efficient and integrated for a better business flow. The result is increased efficiency, better use of employees’ time, and less dependency within an organization.

Better Data management

Data is stored at one center and in different formats, making it easy to manage data as different tools can analyze data easily and use it when required in the native state. Also, it eases up the accessibility of data for all the departments according to their need as all the information required by them could be found in one place.

Better AI and Machine Learning capabilities 

Replicating data to a data lake gives a powerful base for Machine learning and artificial intelligence. AI and ML work on diverse and large data sets; they use statistical algorithms to learn from historical data to get trained for making inferences about new data. With higher amounts of data, Machine learning can be trained in a better way to give more accurate and precise information for the new and coming data and give better forecasts.


Unused data represents unused opportunities that many organizations are letting go of because of technological or other constraints. In a sense, this failure to use data also makes big data collection, which is a big exercise, a partial failure. Though the investments needed to tap data potential may be costly, the effort is worth the investment. And, even if organizations choose to sit on unused data and do nothing, they are exposing themselves to several risks, as described earlier. The key is to do something about unused data and not treat it as a dead, useless thing.

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