No business, large or small, can afford to employ the services of dozens of data scientists or professional analysts. Budgets are tight and there are so many places we need to spend our money to help the business success and grow.
There was a time, not so long ago, when predictive analysis, business forecasting and planning for results involved guesswork and lots of unscientific review of historical data. But, today’s competitive business landscape and rapidly moving markets demand more than guesswork.
If your enterprise is entangled in complex data preparation and manipulation, and you want to simplify and expand the use of data preparation to leverage data integration and self-service data prep, you need to explore the potential of augmented data preparation. Data extraction, transformation and loading (ETL) can be a complex, time-consuming process, but self-serve data prep is ETL for business users.
The benefits of advanced data discovery do not have to be limited to data scientists or IT staff. If you choose the right data discovery tool, your business users can enjoy the benefits of confident business decisions, shared analytics and a common approach to, and understanding of, data-driven, fact-based metrics and results.
No matter the market or type of business, there is no room in today’s business landscape for guesswork. You can’t get a business loan, join with a business partner, successfully bid on a project, open a new location, hire the right employees or plan for the future without predictive analytics.
Don’t think that advanced analytics is too complicated for your business users. Nothing could be further from the truth! You can create Citizen Data Scientists and improve the analytical abilities and outcomes for your average business user if you give them the right tools.
Natural Language Processing (NLP) is a trend that has taken over technology. Vendors are applying this technique to all types of searching and Advanced Analytics is no exception.
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining.
This article provides a brief explanation of the ARIMA method of analytical forecasting.
This article provides a brief definition of the multinomial-logistic regression classification algorithm and its uses and benefits.
