Businesses are generating large volumes, as well as variety, of data at a rapid pace. In addition to the transactional data generated through interaction with customers, suppliers and operations, a tremendous amount of data is being generated through web logs, RFID sensors, unstructured textual information from social networks, mobile phones, smart energy meters and other industrial machines. Making decisions based on this data becomes more complex as the volume increases. The Profoundis Business Analytics service helps businesses analyze the data and take smarter decisions for the future.
Business Intelligence and Analytics
Is it time for me to think about Business Analytics?
THE ANALYSIS FROM DATA IS GETTING DIFFICULT
The data is being spread across a spreadsheet or on a sheet of paper. The analysis phase is getting increasingly challenging. A better tool or visual representation or both combined can prove better in the analysis.
UNABLE TO FIND RELEVANT INFORMATION FROM THE JUNK DATA
The data from different modules/departments of the enterprise is getting larger is size and becoming complex all the while. The identification of relevant information for decision making is claiming a lot of time and proving to be a costly affair.
HISTORICAL DATA AND PATTERNS BEING LOST
It has been a while since the enterprise has been running. The patterns from the previous years can be instrumental in planning ahead. Spreadsheets/Legacy systems are failing to summarize or dashboard the historical patterns.
UNABLE TO IDENTIFY THE WORST AND MUST CONCENTRATE PROBLEMS
Being an enterprise, the problems can come from all nuke and corners and the prioritization of these problems are essential to solve the same. The relation between the departments and the impact of the problems across the modules and the larger impact on the organization is the main factor in prioritizing these problems.
REPORTING GETTING DIFFICULT
The reporting is proving to be difficult at the end of the Financial year or related periods.
What can Profoundis do for you!
The globe is a village growing in competition in all aspects. It is pretty straight. The simple reporting doesn’t suffice. Today's organizations rely on data-driven decision making. The legacy applications like spreadsheets cannot provide the automatic, valuable, insightful data from the raw one. The larger companies around have already leveraged the beauty of data mining. However, the opportunity was out of hand for SMEs. Those days are past. SMEs, today, are making use of the Business Intelligence & Business Analytics platforms to receive service better than simple reports.
The different hierarchies of management of the organizations have different privileges and should have different access to the data. However, it should be possible for the different level hierarchies to collaborate and, share data and perform the analysis. The Analysis may be exported or visualized.
The data from different departments of an organization is complex. There might be different perspectives, different non-obvious co-relations, and multl-dimensional. Though the conventional methods might make sense, it doesn’t convey the entire picture of the data. ‘Which for what’– Selection of the right visualization for the right analysis and your data will make real sense for you.
The hidden co-relations in the multi-dimensional data sets can be easily and automatically identified. Many at times, the associations might be stronger than expected. The co-relations help in further analysis of data. Automatic computation and visualization of correlations between numeric fields can be done. The Correlations between each pair of numeric fields will be calculated, and rendered as a graph. Filtering, area selection, and grouping makes it further possible to easily identify higher degree if co-ordination.
Generate reports with multi-level grouping, and multi-dimensional analysis. The different data element may be plotted across X & Y axes. Each matching between a vertical and horizontal group yields a different chart. Users can choose and easily switch between plot, bar chart, pie chart, graph line, and heat map visualizations. Drilling down and drag-grouping may also be done.
The impacts of each data element on others and the conditions are important when considering the changes of effect. These can be automatically determined.
The data from sources might need to undergo a variety of changes (or filtering) before it can be converted to analysis data. The transformed data can be easily prepared by specifying a set of transformation steps. Create, edit, rename, delete, or convert existing fields by applying the different transformation rules.
Current and historical trends are easily available. Furthermore, it is possible to forecast/predict the future trends like seasonal variations, underlying general trends, or user activity patterns.
The broad categories of records may be automatically identified using algorithms. The algorithms find groups of records sharing common features, and generateinformation for each group.
The access levels of the users can be kept under check for different workspaces and data sets (ex: grant read, write, or full access). The data shall be stored encrypted. The data access will be provided on multiple level authentication checks. Security is enforced at the server level, so there is no way to bypass access checks.