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Data Science at Scale

The challenges that data science faces today are far more complex than ever before. Because of the increase in AI and machine learning, we are able to create new business models and increase revenue and customer experience. Companies around the world are taking part in these increases and using it to make their business become even better.

The changes in big data allow us to improve:

  • Product Recommendations: We can calculate the products that will drive customers higher.
  • Smart Logistics: We can improve management and efficiency within all of our company branches.
  • Product Development: We can quickly identify the best new products and shorten the amount of processing time.
  • Customer Loyalty: When we can accurately identify the next best product, shorten the time-to-market, and increase efficiency, we will be able to improve the customer experience, thus building customer loyalty.

Although this all sounds fantastic, there are some bumps in the road to get to this. 6 specific bumps, in fact. These are data growth, infrastructure complexity, disparate technologies, disjointed analytics workflow, siloed teams, and protecting the data.

These challenges are not impossible to get over, but they are still difficult. With data increasing so speedily, you need to stay on top of it and up to date. When trying to be the most data-driven company, it is easy to get caught up in the complexity of multiple technologies and their expenses. To overcome these obstacles, we need unified analytics. A unified approach to this can allow data scientists to focus on data and collaboration. All teams can focus on their important tasks, rather than the issues that come from siloed teams and such.

There are many parts to making big data unified, and many hurdles and hoops. To learn more about data science at scale, read below.

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