Data is still one of the organisation’s most valuable resources. It is also still a resource that remains largely untapped by most and it’s costing them money. Those 44 zettabytes of data that make up the current digital universe are not being used to their full potential. Traditional systems are struggling to manage the unexpected volumes of data, and the data itself is sinking into swamps that are hard to navigate and manage. There’s a dire need for solutions that don’t just drop the business into yet another complicated bundle of technology, or talk animatedly about structured and unstructured data before wandering off and leaving the confused business to its own devices. It is time, according to Sean Raubenheimer, General Manager at Atvance Intellect to cut the costs and the complexity with simple and effective solutions.
“Times are tough, economies are struggling, and the 2020 legacy is still hanging over business and budgets,” he adds. “Companies want solutions that have heard their complaints about complexity and cost and limited value for money. They also, really, just want data to get on with it, to stop being an expensive problem and start being an asset.”
The reality is that the value extracted from data isn’t cheap. It comes at a cost, and that can sometimes be astronomical, depending on the size of the company and the type of data. It’s expensive to manage data lakes and ensure that the insights don’t turn into a useless swamp of information. But, on the flip side, companies can’t afford not to engage with their data and insights. Those that ignore the vast oceans of data are those that are most likely to be left behind in the age of insights where information is a commodity and the leaders are those that translate it intelligently.
“Companies want a solution that will reassure them that they’re using their data to its full potential,” says Raubenheimer. “They want reassurance that they’re ingesting and storing it properly, that they’re extracting enough value, and that this isn’t going to cost them more than the data is worth. So, they invest into solutions to connect the data to the business. But these data pipelines are extensive and every time the business introduces additional data sources, they create new pipelines that delivers information to separate siloes within the business, and this is the data daisy chain that creates complexity.”
Often, companies have multiple pipelines, each one bringing data into different divisions of the business and creating endpoint fatigue. Multiple collection points and pipelines also demand more resources to manage them, more infrastructure to cope with them, and more costs added to the creaking bottom line. The business needs to stop and take a top-down view of historical complexities and data pipelines and unpack how these can be refined and adjusted to deliver measurable benefits over the long and short term. And there are benefits. Cost savings are an immediate value-add, as are time saving, efficiency, improved insights, real-time decision making, and transformative analytics.
It’s an understandable problem – it’s easy to see how companies were caught in these loops and how challenging they are to manage. But it’s time to clean up the data, remove the complicated bits, and make insights and information accessible,” says Raubenheimer. “This is where Cribl steps in. Yes, this sounds like yet another solution to add to the existing daisy chain. But it isn’t. It’s a proven tool that will solve at least two of your biggest data problems in just one byte.”
Cribl pulls in the data from any source, any vendor and any ingestion method so the pipeline is centrally managed with one technology and one resource. There’s no need to have multiple teams spanning various technologies, it’s an accessible platform that centralises data processing and sends it to the correct destinations and effectively solves the problem of ingesting data from multiple sources and routing it to multiple destinations.
“Whether the data is in the data centre or the cloud, the business has to spend money to have a cluster of compute that can control and ingest the data,” concludes Raubenheimer. “With this solution, the data is trimmed at the source, the pipelines refined, and the garbage removed. In short – the data that the company commits to disk is clean and relevant. The data that’s stored is the data that’s relevant, and that saves time and money. It also ensures that the value, lost in swamps and pipelines and dark data complexity, is unwrapped and unpacked properly.”
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