Data Automation

Why is data automation important?

Switchboard Jun 21

Table of Contents

    We live in an increasingly data-rich world, often referred to as the age of ‘big data’, so it’s no surprise that the automatic handling of this data has increased concurrently. But how exactly is data automated? And why is it so important?

    Source data automation examples

    While data automation usually involves waiting for a data set to be ingested and then processed, the automation stage can also involve the direct capture of data at source. For example, when a POS (Point of Sale) scanner reads bar codes at a store’s checkout, this information can be automatically used to update sales figures and inventory. In this way, automation can be performed end-to-end without any data entry steps.

    OCR (Optical Character Recognition) has various uses in source data automation, such as in the automatic entry of check data in banks, and other instances where standard documentation can be scanned. Recent developments in AI have even extended this to HCR (Handwritten Character Recognition).

    The five advantages of data automation

    The reason that data automation is important, and why it is so widely used, is due to its wealth of benefits. You can read more about the advantages of data automation here, but here are the main points:

    1. Faster turnaround – Computers complete repetitive tasks much faster than humans can, so automation will drastically speed up operations. This means less waiting around before moving ahead with the next stage.
    2. Increased efficiency – With faster processing comes greater efficiency. Not only will tasks be finished sooner, but the high availability of ‘finished’ data, such as sales reports for management, mean that vital information can be accessed earlier and important decisions implemented sooner.
    3. Reduced errors – Even the most capable and diligent worker can make mistakes, whereas automated processes will repeat the work in exactly the same way, every time, eliminating human error.
    4. Reduced running costs – Naturally, increased processing speeds and greater efficiency will lead to less expense. The savings involved arise because the cost of correcting errors, and dealing with the resulting failures due to them, is far higher than preventing them in the first place.
    5. Increased productivity – Although parts of some roles are beginning to be replaced by data automation, there is a large area of middle ground wherein many roles involve some degree of manual data processing, such as finance teams entering supplier invoices. Applying data automation to each of these use cases can free up team members to carry out work which has a much greater positive impact on the business.

    Three challenges of data automation

    While data automation obviously brings huge benefits, it also raises some challenges that teams need to overcome as they consider how to implement it. Here are three common obstacles which usually arise:

    1. Set-up cost – Companies often cling to manual data processing because they get sticker shock when presented with the total cost of an automated replacement. While the initial capital expenditure can be high, usually the savings in running cost more than make up for this. In 2019, Goldman Sachs estimated that automated data capture incurs costs of only about a third of manual equivalents, which proves it’s worth the investment in the long run. However, you should look for a data automation partner who can implement the technology and deliver on your ROI quickly.
    2. Solutions may become redundant – If a data process changes, or the scale of the data escalates, this could cause a bespoke solution to become redundant, which introduces new set-up costs. To overcome this problem, software (and ideally, hardware) should have the capability to adapt to new situations and use cases – and soaring data volumes – with ease.
    3. Fears over employee displacement – Automation necessarily makes some tasks redundant, but this needn’t have a negative impact. For those who find some of their more mundane tasks have been taken away, companies can offer them the opportunity to re-skill and move into other areas of the business, working on much more rewarding initiatives.

    Types of data automation

    Naturally, data automation can be applied to a plethora of different processes, but here are the main data automation pipelines that are used:

    • Batch data pipeline – Transfer or process a large volume of data from its source in a single operation. This is usually carried out periodically, such as moving data from a CRM (Customer Relationship Management) system to a data warehouse on a weekly basis.
    • Streaming data pipeline – Transfer or process data continuously as it is created. For example, real-time online shopping data can be fed into an ML (Machine Learning) algorithm to update product recommendations.
    • Change data capture pipeline – These only transfer or process the differences made since the last pipeline operation. Change data capture pipelines are often used to sync multiple cloud services which share the same data.

    Data automation is important for your business because you can’t afford not to reap the benefits in a competitive landscape. In the age of ‘big data’, volumes are increasing exponentially and you need to be able to adapt and scale – manual processes simply won’t cut it anymore.

    If you need help unifying your first or second-party data, we can help. Contact us to learn how.

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