Enterprise Insights From Big Data Clearing Some Obstacles

Creating Game Changing Insights

The art of insight hunting requires investment in skilled resources, data and tools just to create the chance of finding the nugget of value. Working with large enterprises we often encounter obstacles that lower chances of success. Extending traditional Reporting and Analysis to Big Data projects exposes these problems.

The Typical Enterprise Approach to Sales Reporting and Analysis

  1. Marketing, sales reps, customer service and professional services enter interaction details into their various systems
  2. Each transactional system creates operational reports (e.g. Call volume is used for staffing purposes, emails and calls are logged to track and progress deals)
  3. Combined data from those systems is transformed and loaded into a data warehouse, usually by IT
  4. Business intelligence tools are used to analyze data across the functions to produce reports and cubes of data for analysis and visualization, usually by the BI team
  5. Analyses outputs are shared via production dashboards and enterprise reports, usually via Business power-users

Challenges We Have Seen

  1. IT is too busy: IT’s primary directive is to maintain uptime and to ensure there is a safe and secure application infrastructure to support business process. Mission critical systems projects will comprise much of the backlog. More speculative data analytics project ideas, unless prioritized at the executive level, tend to fall to the bottom of the list.
  2. BI, Reporting and Analytics does not fulfill: Enterprise BI teams maintain the data warehouse and the reporting applications to respond to internal business customers.The business requests received are focused on providing management oversight of functional silos. Game-Changing insights however, are cross-functional, requiring different cuts of broader data; require data to be brought in from the outside. Enterprise BI as a function is not designed to proactively deliver big data insights
  3. Data wrangling effort is too great: As the insight generation requires combining different sources of data,and where data is siloed joins will require pre-transformations or drawing inferences before combining the data. The considerable effort required to carry out the data preparation task for insight generation could be too much overhead for the possibility of a result.
  4. Data politics: Good data analysis reveals anomalies and inferences that may expose functions that have kept certain facts out of the spotlight (for example, late payments from customers). Political blocking of any analysis efforts may come as a shortage of experts who understand the data or stalling because the data cannot be made available.
  5. A belief that the systems and insights already exist: They may actually exist in the minds of certain expert individuals who do have a view of the data or in spreadsheets on individual machines – but not shared across the organization where they can be used (an insight not shared and acted upon is useless).
  6. Data Quality: Data may not be being entered correctly or in time, this is less true systems where the process demands accuracy(e.g. Finance or Inventory and order management), but sales data is typically lower in quality as there is no tangible issue (deals will close even if the interaction records are not updated – although the ability to accurately forecast may be lost)

Removing the Obstacles

  1. IT being too busy – Reduce or eliminate the need for IT, except for governance and where IT needs to extract data from source systems
  2. BI team unable to fulfill – Reduce or eliminate the effort from the BI/Analytics team. Where analytics outputs are being pushed back into the data warehouse
  3. Data Wrangling – Leverage a new breed of data preparation tools at both the end-user level and at the full production level
  4. Data Politics – This is what it is – no amount of technology will resolve this – however, senior executive mandates and a degree of amnesty would help
  5. Belief that analytics already exists – Make sure your Analytics platform can easily consume data from any systems including MS Excel and that production quality dashboards are securely deployed through the organization
  6. Data Quality – Implement governance at the source level, but also allow data to be analyzed quickly and to be cross-referenced quickly hones in on quality problems so that remedial action can be taken

About MoRevenue

MoRevenue is a flexible analytics solution that helps mid-size enterprises leverage data already available to them to improve revenues, profitability and sales process effectiveness. The heart of the product is MoData’s proprietary insight engine that is able to efficiently combine data sources and generate new ‘big data’ insights that are not generally offered by standard sales analysis reports.

MoRevenue begins with ingesting standard data sources, particularly common SFA’s such as Salesforce.com and Siebel and the ecosystem of sales, marketing, customer support and finance applications. There are other Sales Insight applications on the market with similar capabilities out of the box, but MoRevenue is built on the MoData Alchemy Platform that will enable business users to quickly extend the application with new data sources and analyses. This allows the Sales Analytics department to explore further insight on their own without the need for IT support or any customization from the implementation partner.

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