How to Maintain Data Hygiene Across Cloud and On-premise Systems

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In this special guest feature, Javeria Gauhar, Marketing Executive at Data Ladder, looks at some of the challenges encountered while cleaning data in a hybrid environment, and how to maintain data hygiene across cloud and on-premise systems. Javeria, an experienced B2B/SaaS writer specializing in writing for the data management industry. At Data Ladder, she works as Marketing Executive, responsible for implementing inbound marketing strategies. She is also a programmer with 2 years of experience in developing, testing, and maintaining enterprise software applications.

Although most modern enterprises are aiming to adapt a 100% cloud infrastructure, 69% of companies still choose multi-cloud or hybrid cloud architecture. Hybrid environments serve a combination of SaaS vendors (Salesforce, HubSpot, etc.), PaaS vendors (such as Microsoft, Amazon, Google, etc.), and on-premise systems. There are several reasons for an organization to choose a hybrid setting and resist a 100% cloud deployment, such as concerns about costs, data security and privacy, governance and compliance, and lack of expertise regarding cloud computing. 

Hybrid deployments allow companies to overcome these challenges and find the sweet spot that works for their IT infrastructure. But when data resides on multiple disparate sources, it becomes quite complex to use this data for analysis and processing. One of the main roadblocks to seamless data processing is bad data quality. Without a data hygiene strategy and solution that is adaptable cross-environment, it is almost impossible to utilize data for value creation in a manner that is readily available to all stakeholders of a business. In this article, we will look at some of the challenges encountered while cleaning data in a hybrid environment, and how to maintain data hygiene across cloud and on-premise systems. 

Challenges of data cleansing in a hybrid environment 

To gain a complete and comprehensive view of your company’s data stored in a hybrid environment, you may want to bring data together from different sources. This process can introduce some basic challenges such as: 

  1. Handle varying data attributes – Data records kept at different sources may contain the same information but the metadata titles can be different. For example, customer records stored in your Salesforce cloud may contain a Primary Address field, while the on-premise or local records may contain the field Address only. While cleaning, merging, or linking these records, you may need to identify the data attributes that are titled differently but represent the same information. 
  2. Maintain uniform standardization rules – It is necessary to create transformation rules for your data that can be used cross-environment. Otherwise, a lot of time and effort is lost in developing and maintaining the same transformation logic separately for each environment. With multiple rules in place for achieving the same standardization results, the chances of encountering inconsistencies and inaccuracies increase. 
  3. Perform records matching and linkage – One of the biggest challenges of storing data across cloud and on-premise solutions is data matching and linkage. For comparing data records, you need data to be available in the same data type and format. Moreover, you need a technology that provides a uniform interface for comparison and leverages industry-standard as well as proprietary algorithms for matching data values. It may be difficult to “exactly” match data kept in different environments, and other techniques like fuzzy matching, phonetic, numeric, and domain-specific matching may result in more accurate matches.  
  4. Develop a single source of truth – When it comes to data analysis, companies employing a multi-cloud or hybrid deployment wish to achieve one outcome: a single view of their company’s data – the master record – accessible to each department. If some of your data reside locally and some is processed on the cloud, different teams at your firm may look at two versions of the same data. If this is the case, then the marketing team cannot use sales data, and the sales team cannot use finances data, which results in affecting your organization’s performance and productivity as a whole. 

How to maintain data hygiene in a hybrid environment? 

Organizations employing a hybrid infrastructure must implement a consistent and reliable data quality management strategy, that fulfills the need to integrate, combine, sort, clean, and monitor data across various environments and data sources. In addition to the basic data cleansing options, you need a solution that has the following features as well: 

  1. High connectivity options – In a hybrid environment, your data resides in multiple locations such as public or private cloud, and local servers. For you to get the most out of each of these sources, you need a solution that offers out-of-the-box connectors for seamless integration between different data systems. 
  2. Security – One of the biggest challenges of connecting multiple data systems together is security. So, you need a solution that employs a secure method for moving data between cloud and on-premise locations. Moreover, an important feature would be reusing data from disparate sources without affecting or updating the data at any source location. 
  3. Scalability and performance – With the number of data sources increasing exponentially, it is important for a hybrid data cleansing solution to not only support these data sources but offer powerful performance and scalability options when needed. Many solutions demonstrate reduced performance and give inefficient results when integrated with multiple, high-volume data sources. 
  4. Single point of operational control – It is necessary for a hybrid data cleansing solution to provide a uniform interface for operational control. Along with the ability to pull data from different sources and applying standardized rules for data cleansing, transformation, and matching, the solution should also have the ability to push the resulting golden record back to a designated source, from where the data is accessible whenever and wherever needed for analysis and processing. 

Conclusion – Adopt a hybrid data cleansing tool and improve operational efficiency 

For a data cleaning strategy to work effectively in a hybrid environment, you need a tool that offers seamless support for various cloud and on-premise databases. Quality data does not only help you to gain reliable insights for making data-driven decisions, but it also improves customer experience, eliminates duplicate efforts for keeping data clean, and increases overall operational efficiency. 

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