How can you scale real-time decision-making as your data grows?

All data is not the same. Making decisions with data goes well beyond pouring through spreadsheets, numbers, and graphs. This is especially true if you have dozens, if not hundreds, of data sources in disparate formats and need to scale your growing enterprise.

If you want your data to tell you a story and help you drive revenue, you need to start by turning your raw data into foundational data. And the best way to get there is through automation. Here’s how it works.

Getting your data in order

Everyone starts with raw data. This data is messy, chaotic, and disorganized. Each data source is in a different format, has duplicates, and is sometimes inaccurate. It is virtually impossible to make informed real-time business decisions with raw data.

Turning this raw data into foundational data is your first step to using data effectively. Reliable foundational data is easily accessible and actionable. It is clean, verified, in a single format, easily queried, and available in real-time. It is always ready for analysis.

However, turning raw data into foundational data isn’t sustainable through manual extraction, transformation, and loading (ETL). This is because creating foundational data manually requires enormous amounts of labor, which is extremely difficult, expensive, and error-prone – not to mention almost impossible to scale. The best way to get access to foundational data at scale is with automation.

Foundational data at scale

Data automation will enable you to transform your raw data into foundational data at scale. It will eliminate corruptions that might have resulted from incorrect or unreadable data. It will remove accidental duplications of data to ensure you are getting the most accurate information available. It will also free data scientists and engineers to do more interesting, strategic work rather than manipulate spreadsheets all day. Business users and analysts can make decisions with more confidence, and all in real-time.

For example, one Switchboard customer integrated over 250 marketing channels in one month by using our data engineering automation platform. They did not have to hire a single new data engineer to get their foundational data. If they wanted to build a manual system in-house, it would have taken them 18 months and they would have needed to hire expensive and hard-to-find data engineers. In addition, the manual solution would have produced error-prone and out-of-date data. Thanks to automation, this customer now has a 10x increase in the total amount of clean data under management and can make informed spending and advertising decisions – as well as future predictions – with real-time, accurate data.

Scaling with automation

Ultimately, it comes down to scalability. If you are looking to scale, you will need to get a handle on your data through automation. It is ill-advised to hire a bunch of expensive data professionals to extract, transform, and load your data manually. In fact, in my experience, if you do this, scaling your business will be extremely difficult. Creating foundational data with automation needs to be central to your data strategy, and ultimately your business, so you can make better decisions with data.