Turn your company into a data science-driven business in 6 steps
Transforming your business with (big) data analytics and data-driven insights is not a one-time event, but a journey. Here are 6 steps to help enterprises become data-science driven business and enjoy benefits along the way.
1. Explore opportunities
Chances are, your organization is already creating and collecting a lot of data in a variety of forms. Your staff could be analyzing them somehow, but not necessarily in the most effective way. You need to identify the area of the business with the greatest potential, which can possibly deliver valuable insights from the implementation of big data and analytics. Another way of looking at business opportunities is to identify business challenges and information required to solve them.
2. Look for more data sources and explore data
You can give access to all data to your analysts while considering data privacy and industry specific regulations. While exploring opportunities at an initial stage, you will come across a lot of things:
- A huge volume of data which is just amassed but never used
- A variety of data (including structured and unstructured)
- New data sources that generate some kind of data but you never thought to record them
- The data that you are using to retrieve some information are not stored in the best way. To put it another way, the data is stored in such a form that they cannot be utilized to their full potential to generate insights.
Above mentioned findings are just a few examples. You can overcome such shortcomings by creating a technology blueprint that can help you define the scope of big data and analytics in your organization.
3. Introduce new technologies, inject analytics, and drive business intelligence
The technology blueprint will help you build a common understanding of how you want to strategize the implementation of data science solutions (including big data and analytics technologies) and drive business intelligence. The blueprint may talk about replacing your existing data warehousing infrastructure with big data solutions, or migrating from RDBMS to NoSQL, etc. However, the current situation may not allow you to make such changes and it is completely fine in the early stage. There are other possibilities as well, for example, you can complement your existing data warehousing infrastructure with a data lake based on Hadoop and store all your data in a more natural state. This data lake can be your storage repository that will hold a huge amount of raw data in its native format, including structured, semi-structured, and unstructured data. One more possibility is to use ‘dark data’ to generate more insights and accelerate confident decision-making. Make sure you keep security, privacy in mind at this stage, because bypassing them now could make you do significant rework later.
Alternatively, you can take help from data science consultants who can help you analyze your current business situation, break through bottlenecks, develop a clear technical architecture, determine the necessary platform capabilities needed, and deploy it as you move further towards an implementation stage. There are many deployment options including:
- In the cloud (private or public)
- As a service
- Hybrid—a mix of cloud and on-premises or other deployment methods
4. Develop an implementation plan
Many a times, it happens that a company loses sight of the ultimate goal: to evolve, get better and transform into a data science-driven business. To prevent this, you should start with developing a Proof-of-Value program. It will help you validate the ideas and requirements associated with data science initiative, as well as define the expected outcomes. The PoV program will help you understand your project’s usefulness, the skills required to capitalize on your data sources and analytics, and how it will deliver a solid business value.
When you are at the start of your journey, you should identify top business imperatives and associated elements or processes. To realize the power of big data and analytics and understand the impacts of possible scenarios, you can choose either of below mentioned use cases.
If you’d like to learn more about data science use cases, click here.
5. Explore new ways to use your data by expanding to additional use cases
Once you have achieved the desired outcomes from the established use case (or use cases), the next step is to scale a data science solution into other areas. In case you are not getting desired results, you can fine-tune the data science model you have implemented and make necessary modifications to gain actionable insights from your data. You can then share best practices and lessons learned to expand to more use cases to achieve greater, long-term value.
6. Transform to a data-driven enterprise
Once big data and analytics has become an integral part of your business processes, they will power every process and drive every decision. And this is how you can transform your company into a data science-driven business in the true sense.
Planning your own journey
The above mentioned steps are just a typical journey and it may vary from company to company, depending upon business conditions and imperatives. Nonetheless, harnessing all data, translating them into useful information, and generating actionable insights to enhance decision-making will remain constant. Though the transformation requires in-depth knowledge of technologies and understanding of datasets, it will set your business apart in an increasingly competitive market.
If you have got any queries or opinion about data science, please feel free to send us an email at firstname.lastname@example.org
- Data is Ugly – Tales of Data Cleaning
- Data Hierarchy of Needs
- The State of the Text Analytics Industry – 2015 White Paper