The most common problems faced by Small Medium-sized Business (SMBs) while thinking of interpreting and making sense of the data in the organization. Even though the Modern technologies that make possible of data collection, analytics to some extent. However, this option is not useful in improving their business. There is a list of key factors that preventing SMBs from converting their data into actionable business insights.
1. Data collection & data cleaning challenges
- Collecting data from their e-commerce site and on their product and customer behavior etc.
- Verifying, cleaning, standardizing, formatting these collected itself become more sophisticated.
- Lack of spending time in reformatting data to be compatible with integration
Solution to overcome from the above challenges
- Multiple API connectors that simply fetch data from their e-commerce sites every 15 minutes.
- Automatically clean the data and do in the built process of Master data management for every fetch of data from the data source.
- Auto-mapping enables the auto reformatting of fetched data defined by the industry knowledge base for the analysis will take care of.
2. Data Integration challenges
- Lack of data management guidelines and procedures
- Technical inability to connect databases or APIs from various data sources together
- Lack of resource to set up a data infrastructure.
Solutions to overcome from the above challenges
- Data set and API connection functionality provides users with an easy, code-free, drag-and-drop interface
- Just pulling data from different sources so it can be integrated into some level of tailored and ready for display and exploration.
- Data set connection also enables capabilities like Joining, aggregating, splitting, calculating
3. Data interpretation or Analysis challenges
- Doing the right analysis, Choosing right KPIs, and metrics to track
- Converting these qualitative data into quantitative data
- Executing the analytics results into business into action items
- Getting the answer to their current business issue or problem.
Solutions to overcome from the above challenges
- Pre-build industry and functional area specific dashboards interactive visualization with common features like multi-perspective chart options, panning and brushing capabilities multidimensional data with filters, pre-built pivot table option to easily manipulate the data views.
- Pre-build analysis Includes ML and AI algorithms that estimate and forecast.
- Geo-intelligence interactive maps to examine the business.
4. Challenges in automating the repetitive tasks
- Automate the repetitive tasks during analysis processes become very tedious
- Find and do the analyses that will provide actionable insights
Solutions to overcome from the above challenges
- The pre-defined analysis that automatically runs its related process regularly and provides periodic insights as it is required.
- Presentation of results with recommended actions with insights in the respective dashboards
- Pie, bar and line charts and include advanced visualization capabilities
- Heat and treemaps for displaying data emphasis
- Time-motion views for tracking changes over time
5. Adoption challenges
- Fear and frustration with the time cost of setup and implementation.
- Understanding the value and Return of investment (ROI) of implementing analytics tools.
- Hiring data analysts or consultants going to be highly expensive, moreover their priority to run the operations to bring the revenue. Instead of hiring a data analyst or consultant.
- The cost involved in maintaining and managing the system infrastructure for data sources
- Maintain the technical capability to construct a sound data infrastructure ahead of time.
Solutions to overcome from the above challenges
- There is no huge capital investment needed to set up and implement.
- It is going to be an affordable monthly pricing model.
- Inbuilt knowledge base covers the required analytical skill, no need for hiring data analyst or consultant.
- As a cloud-based application does not require to maintain any system infrastructure.
- Natural language search capable AI analytics model does not require any technical or code skill.
