Analytics is an assortment of data collection, data management, data modeling and visualization of potentially large volumes of disparate data. In order to attain data analytics maturity companies should improve their ability to integrate, manage, and leverage relevant external as well as internal data sources and transform them into key decision points. Organizations realize the true worth of their data across every stage as enlisted:
1. Harvest executive support, don’t hunt
Executive support is crucial for data analytics initiatives. These executives not only provide the funding but are instrumental in setting the vision and the tone around data analytics and its advantages. They are key figures in developing the data culture required for the overall success of analytics initiative. Every organization need not necessarily have such executive champions on board, but every organization today has one or the other analytic program deployed. It means 'someone' is certainly in charge of it. This 'someone' can interact with various departments and executives to discuss the advantages of analytics initiative, build awareness, and start the process of building trust.
2. 'Boil the ocean' projects
Organizations can start small, by building the proof of concepts – POCs about any of the real business problems to showcase how data analytics solutions can help the company. No need to take up 'boil the ocean' projects as POCs on small but impactful challenges will get attention from others. Step by step success building is likely to be noticed by other executives and will build their interest in the initiative. Gradually it will help in supporting the beginning of a wider data analytics program.
3. Tackle real business problems to show value
The next step is to gain the basic idea about key business challenges and concerns. Look at and identify impactful areas with real business challenges where analytics can provide measurable value. Frame the business problem that you wish to resolve, by putting a structure around it. Problems with relatively high visibility will bring in desired results faster.
Example: Assessing the reasons responsible for the decline in sales might seem to be a vague goal, but it is important to shred down usual reasons of sales decline to narrow down available options. This is applicable even if you are walking the discovery phase, or are all set to use unsupervised machine learning algorithm to explore data analytics.
4. Results should drive decisions & actions
Results to the questions that you answered through analytics or the question you are trying to answer; should drive decisions & actions, making a measurable impact. Knowing that sales have declined is alright, but knowing why they declined would make all the difference. Upon concluding the sales have declined in only one segment of the customer base in a particular region, you can devise an action plan to address that particular customer base. Picking up an area with data that you trust to analyze, for achieving measurable impact is a wise move to make. It will help you to take an inventory of data sources, which will ensure that relevant data exists and is accessible.
5. Collaboration between business organizations
Good collaboration among 'business organizations' is crucial to the success of a data analytics initiative. However, upon gaining momentum, don’t overlook it. If an operation is going to consume the output of a data or IoT project, then do involve them. Forming a task force, on initiating a data analytics project, with representatives from different groups involved in it, helps.
6. Collaboration for governance
It is important for IT and the business to come together & identify team members, and get the process off the ground. It includes everything right from setting policies to appointing data stewards. Business and IT when collaborate; are involved in governance together. The main focus of data governance is and should be to protect data and adherence to regulations and privacy policies.
7. Integrate data and technology
Managing and integrating data and relevant technology components to form a data strategy. Start considering new tools for data integration or modernizing the data warehouse environment and platforms. Know your data and understand your data sources, as data used by an enterprise usually is not standardized or classified. Data intended to be used may not even exist in the organization yet and, even if it exists, it might be in silos. So what makes sense here is to look out for those silos and find out ways to integrate them. You should also start working towards identifying and overcoming political and cultural issues of opening up data to other parts of the organization.
8. Data integrity and reliability
Initially while exploring huge amounts of data it is fine if the quality of every single data element is not up to the mark. However, upon deciding to include a data source in analytics, and if insights derived is going to get into production or used to make decisions, it is mandatory to validate the data and its sources. The best example of dirty data in terms of name, address etc. is social media.
9. Data management roadmap
This is the stage where you brush your shoulders with unstructured data or streaming data. You can deploy advanced data management experts and technologies that support your data analytics initiative. These can be anything and everything including:
1. Advanced platforms such as Hadoop family of products
2. Columnar DBMSs, appliances, graph databases, and NoSQL
3. Stream processing engines and much more
Most of these are available on-premise or on the cloud. When considering these new technologies, think about them as ultimately making them a part of your ecosystem.
10. Stage of moving past reporting and dashboards
Finally, you reach a stage where it becomes important to start advancing state of the art analytics. The one that you deploy will be used for POC or first project. Two options that we can suggest to choose from are:
a. Visual analytics
Conventional business intelligence reports usually provide a static view to some of the metrics; and do not empower users with the flexibility to explore data on their own. So you can say they are not user-friendly. Knowing this and opting out for self-service data visualization provides an easy way to explore data. Also, some of these self-service tools are cloud-based and can give you a head start. On using visual analytics; chances are that you would be required to work your way through data access and integration issues.
b. Advanced analytics
A step ahead of conventional analytics for data projects, you can use advanced analytics for intrigue cases of churn, fraud, predictive maintenance, recommendation engines, IoT, and much more. Predictive analytics has proved to be the stepping stone for organizations planning to jump the bandwagon of advanced analytics. A predictive data model is a comprehensive statistical or data mining solution involving algorithms and techniques, which you can use for both structured and unstructured data sets to determine futuristic outcomes. You can instantiate the results back in business processes to generate alerts or insights or actions; empowering others in the organization to use it.
Developing a new skill set will become essential. You may try and train your in-house teams, but it is a road less traveled. Usually, enterprises partner with data analytics solution providers and this jump starts their processes. There is no silver bullet to attain data analytics maturity. But a serious consideration of the aforementioned steps can help you build quick wins that show value and build support. It will also empower you to take your next steps around deploying data technologies and infrastructure and maturing your analytics program.