The use of business intelligence and business analytics is growing in every industry, business function and in companies of every size. 48% of small and medium sized business CIOs responding to a Gartner survey revealed that business intelligence (BI), data and analytics is one of the technology areas that will have the largest amount of new or additional spending.
Don’t Be Afraid of Self-Serve Data Preparation! It’s Easy. You Can Do It!
Oh, the mysterious world of data preparation! It is daunting and confusing and…wait, no! It doesn’t have to be. If you aren’t employed as an IT professional, a business analyst or a data scientist, you probably see this arena as confusing and intimidating and you probably want nothing to do with data preparation. BUT, when you need a report, or you have to provide a recommendation to your boss in a staff meeting, you desperately need that data and that analysis, don’t you?
One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When business users adopt true self-serve BI tools like Plug n’ Play Predictive Analysis, Smart Data Visualization, and Self-Serve Data Preparation, they can apply the domain knowledge and skill they have developed in their role to create reports, analyze data and make recommendations and decisions with confidence.
Business Users Don’t Have to Be Data Scientists (But Basic Knowledge is Great)
When you launch an analytics solution within your enterprise, you are probably concerned about getting your business users to adopt that solution. If you can’t engage the business user and achieve user adoption, your return on investment (ROI) will be poor! But, it is important to understand that the right augmented analytics solution can provide the structure and foundation for business users without requiring them to have a sophisticated knowledge of algorithms and analytical techniques.
What is Assisted Predictive Modeling?
Assistive Predictive Modeling incorporates complex, sophisticated analytical and forecasting techniques in a self-serve environment where business users can employ tools to guide them through recommended techniques and report formats and ensure that the methods and reports they choose are appropriate to the type of data and information they need.
Why is Self-Serve ETL and Self-Serve Data Prep Important?
If your business spends a lot of time and money on the task of extracting, transforming and loading data (ETL) and preparing that data for analysis, you might want to consider the advantages of self-serve data ET. Self-Service ETL can and should be easy enough for business users so that your business can enjoy the benefits of advanced analytics without hiring a team of data scientists or IT professionals.
Why is Data Literacy Important?
If you have attended technology conferences or read industry publications, you have probably heard the term ‘Data Literacy’. If your enterprise is considering undertaking an initiative to encourage and nurture data literacy in your organization, you may be looking for a better understanding of the concept and the benefits.
Don’t Be Intimidated by ETL and Data Prep. It’s EASY!
When you hear the term ‘Extract, Transform and Load’, does it make you want to run in the opposite direction? ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.
Data Analytics Can Transform Users to Citizen Data Scientists!
The term ‘data analytics can seem daunting to the average business user. Isn’t analytics something that is the sole domain of data scientists? No, actually it is not!
How About Giving Your Business Users the Power to Prepare Data for Analysis?
Can Your Business Achieve Self-Serve Data Prep? Lots of my friends talk about the difficulty of preparing data for analysis and how long it takes to get IT or data scientists or analysts to take on the project, get the data prepared and run reports or perform analytics. Frankly, this problem is a puzzle to me!