Small and medium sized businesses (SMEs) often find it difficult to balance the day-to-day need for data with the cost of employing data scientists or professional analysts to help with forecasting, analysis, data preparation and other complex analytical resources tasks.
A recent Gartner study found that by 2022, data preparation will become a critical capability in more than 60% of data integration, analytics/BI, data science, data engineering and data lake enablement platforms. If this assumption is correct, SMEs will have to find a way to satisfy the need for advanced analytics and fact-driven decision-making, if these businesses are going to grow and compete.
Data is everywhere in modern organizations and small and medium sized businesses are no exception! The tasks involved in gathering and preparing data for analysis are just the first steps. To make the best use of that data, the organization must have advanced analytics tools that can help them analyze and find patterns and trends in data and build analytics models. But these steps can be labor intensive and, without a suitable self-serve data preparation tool, the organization will have to employ the services of professional data scientists to get the job done.
Data prep and manipulation includes data extraction, transformation and loading (ETL) and shaping, reducing, combining, exploring, cleaning, sampling and aggregating data. With a targeted self-serve data preparation tool, the midsized business can allow its business users to take on these tasks without the need for SQL skills, ETL or other programming language or data scientist skills.
Augmented analytics features can help an SME organization to automate and enhance data engineering tasks and abstract data models, and use system guidance to quickly and easily prepare data for analysis to ensure data quality and accurate manipulation. With the right self-serve data preparation tools, users can explore data, use auto-recommendations to visualize the data in a way that is appropriate for a particular type of data analysis and leverage natural language processing (NLP) and machine learning to get at the data using simple search analytics that are familiar and commonly used in Google and other popular search techniques.
Because these sophisticated features are built with intuitive guidance and auto-recommendations, the user does not have to guess at how to prepare, visualize or analyze the data so results are accurate, easy to understand and suitable for sharing and reporting purposes.
As small and medium sized organizations face the challenges of an ever-changing market and customer expectations, it will be more critical than ever to optimize business and data management and to make data available for strategic and day-to-day decisions. To manage budgets and schedules, SMEs will have to achieve more agility and flexibility and look to the business user community to increase data literacy and embrace business analytics.