However, the use of analytics is incidental. It is the strategic objective that the organisation is pursuing that remains the premise of any analytics project. Data analytics also does not merely serve sales and marketing objectives, which is where it has been traditionally applied; it can be leveraged for every aspects of your business and arguably, in all industries.
In general, the three key objectives that companies look to achieve through analytics are revenue enhancement, operational productivity enhancement, and anomalous activity detection and prediction.
Data analytics can forecast demand and profitability by product, geography, customer, and sales agent. It helps organisations to be more proactive in their planning, and understand if revenue or profit forecast should be higher or lower, the drivers behind it, and the probability of that happening.
Price elasticity and optimisation methods allow companies to derive the optimal levels and timing of product discounts. For example, companies can ensure they always have the right inventory at the right store, and never experience a stock-out when there are demands due to poor planning. Companies can also determine the optimal timing and quantum for each product when planning mark-downs or discounts.
Optimal levels of inventory for machine parts, end products, as well as the best location to place a warehouse can be derived from demand forecast and information on the limits and supply chain throughput. The main benefits are the availability of the product when there is demand, the ability to release working capital from warehouse back to operations for a stronger balance sheet, and an optimised logistics network.
- Operations equipment and machinery
With predictive maintenance methods, companies can determine how equipment behaves and better gauge the demands for servicing or replacement to avoid unplanned or unscheduled maintenance due to breakdowns. As well, predictive and optimisation methods can be applied to maintain cash refill for banks' automated teller machines, enabling better management of cash inventory and manpower.
- Procurement or operation non-conformance
Data analytics can be used to reduce false positive, phase out irrelevant rules and routinely introduce new ones, and more thoroughly identify drivers that may point to occurrences of non-conformance.
- Human capital
Use of analytics can help to better understand and predict employee turnover, performance and development paths.
- Privacy matters
The merits of using data analytics are clear. However, in the quest to improve efficiency and profitability, just as with social media, it is easy to forget that there are people behind the data and that their privacy needs to be protected.
With increasingly tightening data protection laws, lapses on this front can be costly both in dollars and reputation. Hence, organisations should make anonymising big data a priority before using it for analytical activities, including verifying that the sources they intend to use have appropriate permissions from the users who provided the data to perform any additional analytic activities. Remember that third-party risks are your risks too. Even where those permissions exist, it is critical to minimise the exposure of identifiable elements as the data is handled and shared, even within the organisation.
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