6 Checklist to build a data-driven organization
4 mins read
How do you choose a bottle of juice? You might want to check the ingredient list, the expiration date, the nutritional value, and many more. These are the data that help you with your purchasing decisions. Data is omnipresent, omnipotent and omniscient. It is all about how you use data to make reliable decisions.
Similarly, if you are a senior executive in an organization, it is compulsory for you to utilize your organizational data while making any critical decisions. There you have no choice left but ‘not’ to rely on your gut instinct. According to Mckinsey, data organizations are 23 times more likely to acquire new customers and six times as likely to retain customers. Hence, to sustain in this highly competitive world, your organization, employees and customers should have enough data to make better decisions.
Prior to that do you tick all the checklist that helps in becoming a data-driven organization? and the steps to follow before becoming a data organization.
Checklist #1: Knowing your requirements well
You should be aligned with the following requirements before going ahead with the data transformation.
- Company objectives and targets
- Customer behaviour, demand and preferences
- Data quality and control
- Operational efficiency and resource management
The data requirement and company’s requirement or objective should go hand-in-hand. Data requirements mean what data you needed and when you need it. While company requirement is about overcoming one of their biggest challenges or any goal company is trying to achieve. For example, if your company’s biggest requirement is to retain its employees, your data requirement is about collecting and analyzing employees’ performances, training and employee engagement impact in real-time.
In your data strategy, you should also identify the data and insights your customers are looking for. They expect you to share specific information about the product or the brand in order to finalize their decisions. This helps in retaining or getting more loyal customers.
Checklist #2: Sourcing the right data
When you have complex it becomes difficult to tackle the huge amount of data flowing from various departments. When data and storage are integrated into a robust data lake, data discovery becomes easier for employees. Earlier there was an adoption of data warehouses where you collate data from various source systems. Data was then essentially gathered in one location and quickly reported through it. As data warehouses had its limitation, eventually, data lake came into prominence. Data Lake became one single data source from where people can access, assess and adumbrate data. m
Checklist #3: Filtering out your organizational data
Before converting your organization to data-oriented, you must lay down a data governance strategy. Data might include information that is sensitive and highly confidential. A well-defined data governance strategy highlights the procedures and roles necessary to guarantee the accuracy and safety of the data utilized within a company. You must emphasize which data in your data governance plan needs to be carefully handled.
Appropriate data governance strategies ensure that responsibilities and accountability are established within the organization and data strategy are clearly defined. Make sure the tasks and responsibilities of a well-designed data governance system of your data-driven organization must include strategic, tactical, and operational aspects.
Checklist #4: Getting the right technical support
The biggest obstacle to implementing analytics in many firms is not its value but rather how to obtain and comprehend data for actionable decision-making. For companies to reap maximum value from their data, they must deploy the right tools. Platforms like business intelligence help with the same. In earlier days, such intelligence tools have only been able to manage by a group of technical staff. Organizations are enjoying many benefits from self-service BI. Moreover, today’s BI bandwidth has changed to utilizing a complete power of structured, semi-structured and unstructured data.
Employees are upskilling with data-driven skills and connecting data with their workflow and collaborative assignments. However, companies should make sure to build a data organization and data training for employees for improving data-driven skills.
Checklist #5: Is your data appealing enough?
Having bulk information is not enough. It should be meaningful and understandable to everyone in your organization. One of the key steps in the business intelligence process is data visualization. It models the raw data before presenting it so that decisions may be made.
Specifically, data visualization employs visual data to communicate information in a simple, rapid, and effective way. This approach might help employees and decision-makers figure out what has to be changed, what affects customer satisfaction and dissatisfaction, and what to do with certain products.
Checklist #6: Let data talk about your future
Finding the most relevant data when confronted with a particular scenario is a problem that many industrial organizations encounter frequently. AI can speed up this process. AI can find correlations in the data that the engineer was previously unaware of. Additionally, more features like machine learning, reinforcement learning, and other AI advancements are driving a significant paradigm change in how businesses approach everything from tactical planning to day-to-day operations.
Using predictive analytics is among the top analytics solutions needed to cope with this uncertain world. Lumenore’s predictive module, ‘Do You Know‘, automates the insight discovery and foresee future demands and needs.