Big Data vs. Small Data Strategies for Next Generation Business
The data market is booming with an ever increasing valuation. In the virtual data ocean, Big Data and small data are complementary strategies at one level and choices of scale/scope at a different level. While Big Data holds distant promises, leveraging “small data” can provide great benefits to small-to-medium business (SMB) as well as large corporations. Simply stated, the cost-performance barrier leaves many lucrative markets inadequately served by Big Data approaches. Carefully chosen data solutions and models should be more accurate, objective, and ultimately lead to improved ROI especially for more near-term time horizons and/or companies with limitations in scale/scope.
This research provides the reader with an understanding of data management issues, challenges and opportunities relative to Big Data and small data approaches. The report includes analysis of small data practices and emerging small data business models and scenarios. This report evaluates strategies, considerations, and planning for a so-called “small data” strategy. It also compares and contrasts Big Data vs. small data strategies in terms of company capabilities and focus. The report also evaluates the future of Big Data including emerging business models and practices.
SMB of all types
Big Data companies
Social network companies
Telecom service providers
Data services and analytics companies
Cloud and telecom infrastructure providers
Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.
We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.
In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.
We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.
We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.
Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.
Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.
Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.