Businesses should ready themselves to ride the wave and capitalise on potential opportunities that are likely to emerge as a result of these five strategies.
Where are companies now? Many companies view data analytics as a massive undertaking, and they feel they lack the expertise to analyse and interpret the copious amounts of information at their disposal.
However, the first step is to understand which phase of the data analytics journey the company is at, in order to assess what steps can be taken next to better equip themselves with the required knowledge and capabilities.
Phase 1: Discovery
This is the phase where companies are either not aware of data analytics, or are still in the midst of learning and figuring it out. This phase focuses on gaining knowledge and observing what their peers or competitors are doing. Some questions these companies might ask:
- What is data analytics?
- What strategic business objectives do I want to fulfil?
- Where do I start?
In this phase, proper guidelines and standard operating procedures on data governance can be set up, so as to lay a solid foundation for the subsequent phases and to prevent any future duplication of work. The biggest challenge in Phase 1 is usually cost, as the company may need guidance from external experts in the form of advice tailored by industry or function area.
Phase 2: Implementation
Most companies are at this phase. They know what data analytics can do, but they are trying to define the next actionable steps to be taken. Ultimately, these companies want to develop a plan, based on their current data, skill sets and business priorities, to implement data analytics in their organisations.
Companies at this stage generally have employees that are trained in data analytics or a related field, but they are likely to be scattered in their individual teams, managing the data at a department level. The key is to elevate data analytics from department-level to organisation-level. To achieve this, there should be an organisation-wide data analytics strategy that organises the different data analytics teams within the organisation to drive consistency in data standards, ownership and technology, with support from senior management. Senior management should be actively involved in setting directions, driving and monitoring data analytics efforts to give the organisation competitive advantages in areas such as customer acquisition and operational efficiency.
Phase 3: Optimisation
Companies at this phase are successfully utilising data analytics to make strategic business decisions. This is the end goal for companies, but maintaining an optimal level of data analytics is an ongoing process as new data will always be generated, and companies will need to continue to interpret data correctly to ensure that the analysis is robust and applicable.