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Data Analytics
Data Analytics

Data is unlike any other asset your organization owns. It never wears out, it never depletes, and can be used repeatedly at zero additional marginal cost. The value in data is not having it, it's in how you use it. SourceOn IT drives this value from your data with solutions that automate data transformation and machine learning (AutoML) processes and tasks.

Without needing to code, SourceOn IT efficiently and accurately prepare data for Explainable AI that displays the fine details of a model’s configuration so you can better understand how predictions are made. You can be confident your people are making decisions based on trusted, accurate, and interpretable data.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. Data analytics can do much more than point out bottlenecks in production.

Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Our research indicates that while companies have been furiously collecting and storing data, both internal and external, in a laudable attempt to become increasingly data-driven, they have allowed their data models to age.

While the collection, storage, organization, and preparation of vast volumes of new data has been a labour-intensive job, that should not distract from the fruitful work of refining and revisiting the models that companies use to gain insight from all that data. And new tools, automation and artificial intelligence can increasingly do much of the heavy lifting of such tasks.

Chances are, your business is storing loads of dark data—unused information from transactions, connected devices and other sources. And if you're already using automation and AI technologies, you're likely creating more "data exhaust" than ever. How many opportunities are hidden there?

Imagine a steady stream of insights to fuel intelligent technologies; 360-degree customer views to boost relevance and revenue; or faster, smarter decisions to accelerate innovation. Analytics can do that for you.

Digital transformation and innovation is a priority for the senior management team, the necessity of funding the data gathering and insight analytics to drive innovation will fall outside the core budget of most organizations, and they will tend to be limited to scattered pilot projects and one-off proofs-of-concept that will rarely roll-up to enterprise-level returns on investment.

However, with senior-level support, companies can create specific budget lines for investments in data analytics, including not just the machines but in both manager- and employee-level training in data literacy.

But if data is fragmented or low quality, it can't be mobilized. You need to reimagine your data supply chains and processes to ensure transparency, trust and accessibility at speed— only then can data be used to maximize your technology and AI investments.

With data continuing to grow in size, scope and complexity, organizations are hungry for innovative strategies, services, and technologies to unlock the value of their data analytics potential.

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity. We work with our clients to support their missions by developing end-to-end solutions.

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