Become a data-driven company

Organizations are collecting and storing more data than ever before, whether it’s on-premises or in the cloud. However, there are some challenges to overcome before you can improve performance and profitability and implement stronger innovations by mining your data for insights.

  • More accurate information.
  • Meaningful insights.
  • Complex analyses.
  • Outcome visualizations.
  • Improved data quality and information management.

These technical foundations will give your organization the ability to make better and faster decisions, taking advantage of the large amounts of data you collect daily.

Data-Driven Solutions to redefine how you work

We provide forward-thinking solutions to organize, analyze and deliver your data to improve business decision-making, develop new products and optimize internal processes.

Management

A centralized data management system gives you a clear view of data you can act on so you can overcome new challenges.

Analytics

Specialized software empowers users to centralize data in one place to automatically run complex calculations with a single click.

Visualization

On-Demand visualization of data enables users to instantly communicate numerical results to better understand business challenges.

Data-Driven Acceleration

Being data-driven is not an end state. It’s the beginning of an exploration of exciting possibilities. Organizations have to overcome four hurdles to be data-driven.

Unstructured Data

Unstructured data refers to unorganized information such as emails, text documents, images, sensor readings from the Internet of Things, and more. This type of unorganized and heterogeneous collection of data cannot easily be placed in predefined data models.

Unconnected Systems

Organizations often use multiple information storage systems side by side with no — or, at best, unwieldy — connections between them. These systems may offer conflicting information because they use different sources, processing methods, or naming conventions.

Low Data Quality

Sometimes data quality simply isn’t good enough because of poor data input or poorly implemented data connections. It’s hard to get good business intelligence from poor — or incorrect — data.

Misalignment with IT

Business units shouldn’t have to depend on IT for data analytics. They should be able to do it themselves. One reason? IT is under constant pressure to deliver more at lower costs so that data analytics requests may end up at the bottom of their list.

Why not be transparent with consumers about the huge amounts of data that are
collected?

Experts can use big data to further optimize logistics. By taking into account various factors that influence delivery speed and reliability (e.g., traffic flows, accidents, weather patterns, rain storms), smarter logistics could even be used to operate more sustainably.

With the wealth of data generated by machines, your production lines could map
out ways to become even more productive, discover hidden costs and unlikely sources of revenue. The IoT and the Industrial Internet of Things (IIoT) are set to revolutionize production, as well as consumption patterns. That new reality is just around the corner.

Analytics can be put to work for data protection as well. With advanced pattern recognition and correlating behaviors, risks can be assessed better and cyberattacks or real-life security threats can be prevented before they even occur.

Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. AI relies heavily on deep learning and natural language processing. Computers are trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in that data.

Real-time analytics can track consumer behavior and pair it with the right kind of offer bundles to attract attention and address the needs of the individual.

This effectively creates the segment of one.