Love it or hate it, the singer Cher had a hit single with her 1989 song “If I Could Turn Back Time”. While the song may now be stuck in your head, the truth is that developers who work with data now have the ability to rewind time, at least from a data perspective.
The Cinchapi Data Platform (CDP) allows developers to stream and store decentralized or disparate data from any connected data source. The foundation of the CDP is the open source Concourse Database, created and maintained by Cinchapi. Since Concourse is a strongly consistent database, it stores definitive data values from connected data sources.
With versioning included, even if the original source data has been overwritten, lost, or changed, developers and analysts will always have the ability to go back to any point in time to see what the values were at a specific moment in time.
The Benefit of Traveling Back in Time
Data is fast, and data is often messy. By that we mean that data points change and evolve from moment to moment. What was true a minute ago may no longer be true now. Worse, typically data is siloed, so it becomes increasingly difficult to see relationships between decentralized data sources.
In other words, organizations have an enormous amount of data which is constantly morphing in real time, and the sources of the data are not connected to each other. That makes finding relationships between data sets a tedious and time consuming task. Dependant upon the data, we could be talking weeks or even months of data prep and cleanup just to see what is relevant, and how the data sets relate to each other.
By leveraging the power of machine learning, the CDP can make short work of understanding what your data means, and it can uncover interesting relationships between otherwise siloed data.
That’s pretty cool, but it gets even better. With these previously hidden relationships now exposed, the data developer, analyst, or scientist can now explore aspects of the relationship at any point in time.
Think of this as like a DVR for data. Sports fans will often rewind a play to see it again – they want to see how the play developed, who did what right, and who did what wrong to lead to a score or a loss of possession.
Similarly, the Cinchapi Data Platform allows users to rewind data, “press play” and then watch as that data evolves to its current state. Just like a DVR, users can slow things down, fast forward, or pause at specific points in time.
This could prove valuable for a vast array of use cases. Banks and credit card issuers might use this to detect credit card fraud, and to prevent future fraud. A retailer might use it to better understand why demand for specific products rise and fall. A logistics company might use this to determine more efficient transportation routes and methods.
The Visualization Engine
Out of the box, the CDP lets a developer see relationships between her connected data sources. It doesn’t matter what the schema or the source of that data may be, because the platform doesn’t impose any schema on her. She can work with financial data, IoT generated data, data from operations and logistics, or virtually any source to which she has access to via a direct connection or an API.
Good stuff to be sure, but looking at a glorified spreadsheet with values changing over time can be a little off-putting. This is why a powerful visualization engine is included as a core component of the CDP.
Visualizations help people to see the relationships in data. But as we mentioned earlier, typically the data in one data source is independent of other sources. Vendor data might be in one silo, customer data in another, with operations and logistics in still another silo.
Factor in social media data, news events, and a host of other data and the list of potential data silos can be mind boggling as the size and scope of a business grows. Yet as the amount of data grows, it becomes an increasing critical to see the very relationships which could be impacting productivity, sales, operations, and much more.
It’s not just the positive things that can impact a business. We’ve all heard stories of retailers and other businesses which found out well after the fact that they had been hacked, or that fraud has occurred.
This doesn’t just hurt the bottom line, it can also have a profoundly negative effect on the reputation of a business. When retailers like Target or restaurant chains like Wendy’s had customer information stolen, how much potential business did they also lose because customers were fearful of of their information also being exposed?
It’s impossible to put a specific dollar value on bad publicity, but we will suggest that there is a significant cost factor when customers shy away from a company because they fear becoming the next victim.
Data is big, and it’s only getting bigger. It’s also increasingly messy in that not all data is relevant to a specific problem or opportunity. Having the ability to uncover relationships that were hidden is compelling enough. But being able to rewind the data and see how these relationships looked in their nascent stage can benefit anyone with an interest in data forensics.
Cher probably wasn’t thinking about data when she wondered what would change if she could turn back time. But with the Cinchapi Data Platform, anyone working with data can turn back the calendar to see when and how data relationships were established, and how they then changed and morphed over time.