data automation

Data Automation In The Data Fulfillment Process: How It Is Changing The Enterprise Landscape

Revelate

Table Of Contents

Organizations have been using their own data for decades to inform business decisions, strategies, and gain a greater understanding of their performance in a specific market or industry vertical.

But data isn’t as useful in terms of providing the best insights when it exists in a silo, so to gain the edge over your competitors and make better and more effective business decisions, it’s a no-brainer to gather relevant data from elsewhere.

That means more organizations are sharing their data than ever before, whether it be through partnerships with outside organizations or internally to prevent siloed data that doesn’t tell the whole story. Data sharing can be extremely lucrative, too: selling your data to outside parties to augment your finances in times of economic uncertainty or to build mutually beneficial data sharing partnerships with other businesses.

But here’s the kicker: how will you fulfill data orders?

The traditional way is to put the burden on your IT team. They get the request and have to either sift through endless amounts of data on different servers and networks to find what the client is looking for, or they simply send one gigantic file over and leave it up to the client to find what they’re looking for. Sure, it works, but there are big concerns:

  • If there are one or two orders to fulfill, then it’s not as big of a concern to sift through data or send over a giant file. If there are hundreds or even thousands of data orders, then this DIY or manual way of data fulfillment quickly becomes impossible.
  • When sending a gigantic file to a client, how can you control privacy and security? Manually adding access restrictions and other security measures is time-consuming and doesn’t offer the best protection.
  • When data is transferred, copied, or changed to different file formats manually, the integrity of the data can suffer. Ensuring that data remains unchanged as it’s shared is a huge challenge.

If this is your current data fulfillment process, know that there’s a better way.

Data automation.

That’s right, you can automate your data fulfillment process so your IT teams can finally breathe, you can easily control access privileges and other security measures, and the integrity of your data is maintained.

Let’s get into more about data automation, including how it works and how it helps you share your data easier, faster, and following your organization’s established security measures.

What is Data Automation?

The process of gathering, preparing, and uploading data to a marketplace or data web store automatically instead of manually is referred to as automation of data. 

In other words, instead of your IT team having to manually fulfill data orders, data automation allows anyone in your company (e.g., salespeople, marketing coordinators, administrators) to fulfill data orders themselves. It can be game-changing in terms of efficiency.

By completely automating the data fulfillment process from start to finish, regardless of where it’s coming from.

Here is a quick, high-level rundown of Revelate’s data fulfillment process:

 

Process Description
1. Prepare Data is extracted from a source and refined and prepared into consumable data products according to the client’s needs.
2. Package Data discoverability, pricing, and access rights are determined. Metadata tags, documentation, use cases, access rights, and purchase options are set out before the product is placed on the organization’s web store.
3. Fufill Customers make a request for a data set, and Revelate extracts that data from any platform, has it go through the appropriate checks and balances to ensure security and access privileges are maintained, and then provides the data to the customer in the formulation that they require.
4. Distribute Allows you to extract data from any ecosystem (including Databricks and Snowflake), prepare it according to a customer’s specifications, and distribute it to the customer’s ecosystem via API, SFTP, download, etc. so the customer can get the data they way that they need it.
5. Commercialize (if the goal is to sell data) Data products are displayed on a customizable web store for sale. Metadata and tags help organize data products, make them easily searchable, and give users context to their contents.

 

With Revelate, each of the above steps is automated. This allows organizations like enterprises to completely leverage the information that flows through their organization, completely automating how data is accessed, stored, and distributed while maintaining full security.

There are myriad benefits to this, but first, a data automation strategy has to be created to ensure that data movement is handled in the most efficient and secure way possible.

How to Develop an Automation Data Strategy

Before developing an automation data strategy, you need to understand how your organization’s data is stored.

Your organization will use a variety of systems for data storage and access, and may have all or some of the following in place:

  • CRM
  • ERP
  • Data access systems (Immuta, Okera, Privacera, etc.)
  • Data catalogs (Altation, Collibra, Gretel, etc.)
  • Data sources (AWS, Google Cloud, Databricks, Snowflake, etc.)
  • Databases, including Oracle, Redic, Postgre SQL

By default, all the data from these different sources live in the native systems⁠—but if an organization wants to provide easy access to this data, then it must be stored together in a more accessible, centralized location.

There are two approaches to this, and most large enterprises or other organizations need both to handle different types of data:

  1. A data warehouse stores structured historical and present data that is used for analysis, reporting, and business intelligence. Because data is stored in a structured format, it also dictates the analysis that can be performed with it.
  2. A data lake holds raw data in its original format until it’s needed. It’s more flexible than a data warehouse, but the data lacks any sort of structure or clear use case, so data from a data lake is typically used by engineers and data scientists because they have to determine what it is and what it can be used for before moving forward.

Considering all the moving parts involved in harnessing data from either a data warehouse or data lake, including permissions, security, and maintaining data integrity, it quickly becomes clear that a data automation strategy needs to be established.

Here is what you should know when developing a data automation strategy:

Identification of Problems

First, you need to determine where organizational problems lie concerning data fulfillment. If you’re fulfilling data orders, it’s likely that it takes a long time to fufil them correctly. Another common issue is internal teams trying to find useable data to extract for reporting and analytics. In short, you’ll want to consider the areas in your data operations processes that are consistently breaking down or are often faced with roadblocks and consider how automations could improve those processes. Make a list and keep it handy for when you’re executing the steps of your data automation strategy, which we’ll outline in the next section.

Prioritization

Determine which processes are taking the most time and how often they are performed. For instance, IT teams may spend quite a few hours manually fulfilling external data orders because sales have consistently sold data products to new and existing clients. Think about how data automation could save time on a process and how long it would take to implement that automation. Then, prioritize each automation based on difficulty and need.

Execution

To determine your data execution strategy, ask yourself the following questions:

  • What procedures and steps will you need to follow to ensure a streamlined but accurate strategy that will meet your data fulfillment goals better and faster than before?
  • What data automation solutions will your organization require?
  • How will you fulfill granular data orders?

That’s where Revelate comes in. It provides an all-in-one solution that allows you to create granular data products to suit the needs of all your clients and lessens the burden on your data and IT teams to fulfill orders and maintain different data products. Instead, clients can access your online data storage and download existing data products with the click of a button or request a specific data product that can be created and distributed to them in significantly less time than it would take to do manually.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started

Steps to Automate Data in Your Organization

steps to automate data

      1. Data Identification

        Start by taking a few datasets from your different ecosystems and drawing a pipeline from source to distribution. What information is already available that tells the recipient of the data what it is and what it can be used for? How easy is it to extract the data from the sourcing ecosystem, and does the integrity of the data remain intact? Identify any gaps or challenges, and note whether automating the fulfillment steps could be a potential solution.

      2. Determining the Transformation Process

        You’ll need to develop your ETL process with the understanding that different clients will want data in different configurations. Some data is flexible and can be transformed into various formats, while other datasets have restrictions because of their original configurations. Some transformations could be simplistic like renaming files or converting the data from .CSV to .Doc, and others might be more complex.
        As a reminder, the ETL process consists of:

              1. Extraction: Taking data from one or more source ecosystems
              2. Transformation: Refers to the process of transforming data into different structures to suit client needs
              3. Loading: The process of taking prepared data and loading it onto a system for clients to access, such as a data web store or open data portal

        With a platform like Revelate, your ETL process can be fully automated based on your business needs, as well as your established security and access levels.

      3. Security and Access Levels

        Determine how data should be accessed and what credentials are needed depending on how the data is downloaded. For example, the entire data product might be available to download for anyone in the marketing department, but specific portions of that data might only be available for a manager or analyst to use. Specific access levels and other security features should be set out in advance for different situations.

      4. Platform Selection

        Traditionally, data automation would be handled by tools like Python’s NumPy, Pandas, and SciPy packages, used in conjunction with other tools to make data shareable. However, this requires extensive development, IT knowledge, and dedicated resources to ongoing maintenance to keep everything running smoothly.

        Revelate is a one-platform solution that offers no code or low code options to determine access privileges and distribution, no matter where the data originates from. In other words, instead of having multiple scripts, systems, and tools to manage your data automations, you only need one.

      5. Testing the Data Automation Process

      1. Once you have a process set out, it’s important to test it thoroughly before implementation. This means running a controlled scenario and determining if the data was changed or loaded without issue.
      1. Revelate has built-in tracking, so every activity that occurs on the platform for each data fulfillment initiative can be analyzed for effectiveness. If a failed process or blocked access added time to the fulfillment process or caused another issue, then it can be easily identified and rectified at the source.

Benefits of Automating Data Fulfillment

benefits of automating data fulfillment

Provide a Better Customer Experience with Data Fulfillment

Instead of clients having to wait days or even weeks for a data order, automating data fulfillment can reduce this time to minutes.

Picture this: a salesperson connects with a telecom client interested in purchasing historical data to determine their go-to-market strategy. However, the data that the client wants contains the personal information of a group of consumers, and privacy and security issues are strict in terms of what can and cannot be accessed by a third party. While the overall data set is still useful, traditional data fulfillment methods (read: sifting through astronomical data sets, looking up security and access policies, implementing protections for certain data components) would have IT teams working on this request for months. But with automated data fulfillment and a web store, the data product can be uploaded for almost immediate access, due to computers handling the finer details and ins and outs of security policies and procedures instantly.

So now the client can access the data they need with convenience and simplicity, making it easier for large organizations to sell more data products to more people and maintain their security policies.

Saves Money

Not only does automating your data fulfillment process save time, but it also can also save your organization a significant amount of money. Instead of IT teams spending their time sifting through layers of raw data or putting together data packages manually, automations take care of the process for them. In a lot of cases, IT doesn’t even need to get involved in the fulfillment process at all, it can simply be handled by salespeople or even the client themselves through a self-service portal.

Create Just-in-Time Data Supply Chains

Storing large amounts of data in warehouses or lakes can be costly, especially if you’re paying for storage by the gigabyte (ouch!). Data automation supports a just-in-time data fulfillment strategy, providing the right data product to the customer upon demand. Just like a car assembly plant might not keep a specific type of airbag sitting in its inventory but set up its supply chain, so they receive the airbag as soon as the car that needs them comes onto the assembly line, a data provider can do the same thing. As soon as an order is placed for a specific dataset, automations can take effect to retrieve the necessary data from its source, prepare it, package it, and distribute it to the right customer.

Creates a Competitive Advantage

Instead of only using internal data to inform go-to-market strategies, marketing campaigns, reputation management, and endless other examples, that data can be augmented with external data to strengthen its usefulness.

For instance, an eCommerce website could obtain external data regarding their target audience, such as demographic information, interests, hobbies, and more, and compare that information with their external datasets to see changes in audience behaviour and interests over time. This would likely give them a competitive advantage due to their enhanced customer understanding.

Allows companies to “Do More with Less”

Times of economic downturn and uncertainty have forced companies to streamline their resources and take a good hard look at what they can do to make business processes more efficient while being more discerning with their spending. Taking advantage of data automations to reduce repetitive tasks that were once done manually is one way of reallocating resources to areas that need it more.

For example, the frequency of data deduping and cleaning procedures can be lessened through automations. When an employee changes customer information, those changes can be updated globally with data automations. To prevent errors and other issues from occurring, specific criteria can be implemented to prevent accidental or incorrect updates from happening in the first place.

Meet Compliance Requirements

When data is being moved around, issues with privacy and security can easily occur. Regulatory bodies for industries like healthcare and finance have strict criteria regarding privacy laws that organizations must follow to ensure that sensitive information, like a client’s private health records or bank account details, doesn’t get into the wrong hands. Organizations that fail to follow these regulations can be subject to fines and even get shut down if infractions are serious enough.

Data automation reduces human error that may compromise sensitive data and protect who has access to what. Automations also provide consistency, so there are no lapses in handling sensitive data, helping organizations stay compliant.

Data Automation Examples for Different Industries

Industry Data Automation Use Case
Healthcare AI-powered chatbots integrated with RPA bots can enable self-service patient scheduling, including getting information about health problems, setting appointment reminders, and rescheduling and canceling appointments.
Finance Automation technologies enable financial institutions to collect data from any source, and entry, validation, tracking, and reporting tasks that are repetitive to do manually can be eliminated. Further, data automation allows firms to adapt to any changes in regulations to ensure compliance flexibly.
Telecommunications Telecom providers are transitioning to Machine Learning (ML) supported automation systems to explore new areas for revenue growth, including reducing time to market, offering competitive pricing models, growing and providing more reliable connectivity services, and enhancing the customer experience.
Manufacturing The manufacturing industry uses data automation technologies to make factories more efficient and less wasteful, optimize supply chains and logistics, improve facility management, estimate market demands, and much more. This includes data sharing to speed up AI and ML capabilities and provide mutually beneficial insights for the entire industry.

 

Conclusion

It’s clear that data automation saves organizations time and money and helps them be more efficient and productive. The business cultural idea that you have to hold on to all of your internal data at all costs is rapidly changing, with more organizations seeing the benefits of sharing their data. The conversation is becoming more focused on sharing your data unless there’s a reason not to, rather than defaulting to not sharing your data at all.

Data automation has made it easier than ever before to share data safely and securely, with little maintenance. As more organizations adopt effective data automation and put in a bit of legwork at the beginning to set up their processes and strategies, the prevalence of data sharing will only increase.

Revelate provides the perfect solution to handle your organization’s data sharing invites as an all-in-one data fulfillment platform. The best part? The platform is fully customizable for your business’s needs.

Interested in learning more about Revelate? Contact us today.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started