The importance of data for an enterprise is immense in today’s data-driven world. From delivering satisfactory customer service to gaining a competitive edge, enterprises need to have quick access to real-time data. However, getting relevant information for use is not as easy as it may sound. With scattered and unstructured data, companies struggle to manage their data and obtain a single view of all the data they have across various segments.
Moreover, as the significance of data increases, the process to manage data derived from multiple data sources of varying nature gets complicated. Here arises the need for a separate data warehouse that can provide easy data access to reporting tools, analytics and business intelligence (BI) for in-depth analysis. Besides, enterprises need to meet and comply with information governance standards and security protocols while storing, collecting and dealing with data for the business purpose. This overall adds to the complexity and segments data into multiple silos. With information spread everywhere, companies lack a unified and comprehensive system that can give them a single view of all the data in their possession.
Here are some of the ten steps following which can help enterprises get single data view for better optimization of information for their business.
At once, don’t try to fetch every customer data from every system for a single view. When you start unifying your data into one system, rather than targeting all focus on any specific business problem. And then related to that particular goal, pull defined set of data for better understanding.
Hence, you should start pulling data from a specific source that focuses on a particular problem. This will help to narrow down the scope of your project and allow easy identification of major stakeholders within the particular data segment of your data warehouse.
After you have identified data of which business problem you will pull for getting a single view, the next thing you must do is understand the customers whose data you are dealing with. To address their needs, you need to know their requirements first and then act accordingly. Once you know who your customers are and what they prefer, demand and expect, you can offer them a relevant product and win their trust.
The third step enterprises need to follow is identifying all those data sources from where the data for your specific business project comes. Knowing the data source is crucial as it indicates how reliable and accurate the data can be based on its origin. This knowledge about the data sources can be helpful in case of modifying an existing system or automating any previous manually operating system.
Step 4 is the crucial one as now you need to appoint a data administrator to take control of the entire process of creating a single-view project. From ensuring data quality to performing data maintenance activities, the role of the administrator is going to be crucial. As a responsible guardian, it is going to be their duty to ensure use of clean techniques to extract data from the source. Also, they need to help incorporate the data into the existing systems without causing any interruption to the flow.
While creating a single view of your entire data collection, you need to make choices based on priority. Not all the data you have are important. Hence, figure out data that are needed to be indexed so that the process becomes more efficient and faster. For instance, email addresses and postal addresses may be mandatory for your application while social media details may be optional. In that case, you need to put the focus on pulling mandatory data first and then in the process keep on adding optional fields if required.
Now, when you are almost there towards achieving a single-view data model, the next thing to do is define the various data fields. It is your choice how you want to design and standardize data field names for the specific attributes.
The same customer data in your system can be present in different segments. Hence standardizing data is not enough. You need to use algorithms to identify all the information related to a particular customer profile. For instance, you can use unique identifiers to match, merge and reconcile all those fields within the system that may be related to the same customer. With the help of credit card number, for example, you can identify the canonicals and cluster records having similar attributes to find out if data you have is of the same person or not. In doing so, machine learning and artificial intelligence, in particular, can be of great help.
Now begins the deployment of your single-view data model. And for that, you need to ensure an efficient architectural design of the underlying systems. The success of your overall efforts greatly depends on how capable and robust your system is and how well it meets the performance objective and various security aspects in the process.
At this 9th step, you will need to create RESTful APIs so that applications can easily pull data for a single view. You will need to pay attention to systems consuming data and ensure that their functioning is directed towards supporting your aim of the single data view.
The last step involves ensuring that your data model is capable of adapting to changes. Business systems keep evolving, and so your data systems should adhere to the changing needs as and when required. That is why it is crucial to develop a flexible data model that can keep pace with the changing source system and continuously update itself.
Creating an efficient single-view of your data isn’t that difficult if your practices and strategizes are right in place. You just need to understand your specific business requirements and take data segmentation for a single-view at a gradual pace.
Following the above ten steps will help a lot of businesses who are struggling with unorganized and scattered data. Now, you can make the best use of your customer data to reach your business objectives without any interruption. While most of the companies have piles of data but they fail to use it in the right way because of lack of proper approach and data management. However, with these right practices, data management is no longer a thing to worry about.