Should everyone tap into data analytics? What if one doesn't have the right resources?
1. Are Business Goals Properly Defined?
2. Assess All Your Options — Including Not Adopting Analytics
3. Are Your Resources Aligned For Analytics Adoption?
4. Which Data Analytics Option?
5. Fool-Proof Your Data Management Strategy
The adoption of data analytics typically happens naturally within organizations. It occurs depending on the needs and skillsets of the employees. This also means that data analytics is often underutilized and effectively silo-ed to specific departments, or worse, a single employee. However, clever leaders realise that they can scale the impact with data analytics. It can mean getting feedback, making sharper decisions or, even better, automating processes to scale operations.
But how do we even start? We break down the five strategic considerations below to help organizations successfully adopt data analytics in a sustainable way.
Business goals give organizations direction. Goals are an important planning and strategy tool for the leadership team to guide the business. And it is important to apply it towards data analytics adoption. It helps frame analytics adoption with a long-term vision and inspire teams to plan the immediate steps forward.
Besides long-term goals, setting SMART goals is a proven technique for organizations to frame efforts for the short-to-medium term. It should also be re-evaluated from time to time.
Some analytics adoptions goals are:
- “Identify and analyse industrial processes for IoT integration in the second half of 2019 to identify the complexity of each process.”
- “Based on findings of the above goal, prioritize industrial processes to implement by February 2020.”
The above goals are specific with a clear timeline. It gives the business hard targets to measure against in the future. A series of established goals helps to create a long-term road map to success.
Not every business domain will require analytics, and different business units may require specific strategies, tools, techniques, etc.
To manage resources, it is important to prioritise which business units need to implement data analytics based on its applicability. A simple questionnaire can help to evaluate this within the business domain, at a high level you may ask questions such as:
- Is analytics the best solution?
- Are existing solutions sustainable and profitable in the long term?
- Should we adopt a SaaS solution or develop our own?
- Which tools, techniques, strategies, and expertise could be leveraged?
- How will this give us the competitive edge in the long run?
This is certainly not an exhaustive list, and such a questionnaire must be tailored to individual business units.
How prepared are your resources for analytics? Invariably, every organization will have some legacy infrastructure which can't be adapted to any kind of data adoption strategy. These units are either scrapped and redeveloped from scratch, or are left as is, depending on business requirements.
Another consideration is if your organisation is willing to invest in resources training and re-skilling when the time comes? This requires management buy-in. Check that C-level executives, as well as, employees are ready to embrace the transformation. Identify any bottlenecks within different departments and business units. Be prepared to consider organisational restructuring to eliminate such bottlenecks.
In an ideal world, an implementation team would be established; one that has the support of the C-Suite and the power to make autonomous decisions responsibly. There are three major options for consideration:
- Third-party Support – This often plays a critical role in technology adoption. If the organization lacks knowledge and expertise, then enlisting experts can help accelerate transformation by providing counsel as well as identify strategies for adoption.
- Open-source tools vs. commercially available options – not all products fit every task; hence, it is critical to analyse and assess which tool will be best fit for the job.
- Involving in-house data analytics teams – If your organization has spent enough time forming a digital leadership team and have successfully rolled out plans for creating dedicated analytics and data science teams, then it may be the case for an in-house team to drive further implementation.
Various organisations also combine third-party vendor resources along with in-house teams to roll out and implement strategies. With this type of combination, organisations ensure that they get the services of a dedicated subject matter expert who will also help in providing guidance and mentorship to in-house teams for future readiness.
Besides planning for the roll out of different analytics strategies at different stages, it is also necessary to assess the reliability of your data management strategy.
Data management is a vast and complex process. The data that is generated from the management system needs to be processed deliberately at every point along the way. Critical datapoints from identified data sources should be logged for error-checking if needed. Furthermore, a robust data management strategy records all the devices, sensors, and other data collection tools that are used, as well as any methodologies and algorithms in place.
Establishing what happens to the data once it has been collected – how it will be stored and processed – are other significant items that need to be documented for future use by the relevant departments (i.e. IT team, data science team, etc). These processes must be regularly reviewed for their relevancy and efficacy on a continual basis.
Every organization has its strengths and weaknesses. They each need to assess its own processes to identify and understand its readiness for the adoption of analytics and its associated technologies. Keeping the above five considerations in mind helps guide the adoption for the long-term, accelerate growth and reduce operational hiccups. Discover how analytics can be applied to your organisation’s specific needs here.
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