Big Data; Best Practices

 

Organizations have been using data to make decisions and improve processes for a long time. But now that we have such a wealth of information available, data has become the new currency. Organizations are using big data to make better decisions, create new products and services, and cut down costs. In this new reality, we need to be mindful of how we use and extract data, which means developing best practices for big data.

Big data management is a complex process that organizations must face while running a business. Big data challenges can impact the success of the implementation, and this is why it is necessary to discuss these nine big data best practices in order to facilitate the process and stay up to date with the latest trends in big data.

Before we continue, I strongly recommend reading our previous article "big data benefits" to get an in-depth idea of how big data can impact your organization.

What are key big data best practices?

The purpose of big data best practices is to ensure that the data is not just collected, but also analyzed and stored in a way that it can be retrieved when needed. There are many ways to store the data and it depends on what you intend to do with it.

1. Align Big Data with specific business goals: More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behaviour, deriving sentiment from social media and customer support interactions, and understand statistical correlation methods, and their relevance for customer, product, manufacturing, and engineering data.

2. Ease skills shortage with standards and governance: skills and shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.

3. Optimize knowledge transfer with a center of excellence: Use a Center of Excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or an expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.

4. Top payoff is aligning unstructured with structured data: it is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today.

5. Plan your discovery lab for performance: discovering meaning in your data is not always straightforward. Sometimes we don't even know what we're looking for. That's expected. Management and IT needs to support this "lack of direction" or "lack of clear requirement." 

6. Align with the cloud operating model: Big data processes and users require access to broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including:

1. transactions,

2. master data,

3. reference data,

4. summarized data.

Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

Choose proper big data storage locations: another big data practices is the necessity of choosing a location where the data should be stored. Data storage is an important responsibility of the modern business world. With so many data breaches occurring in recent years, it's more important than ever to know where to store your data. Cloud storage is a very popular option for companies, since it's accessible from multiple devices and can be scaled up or down as needed.

1. Keep your data in close proximity to where it is used and referenced.

2. Place your metadata (information about your data) with the physical file that contains the actual raw data.

3. Store your data in multiple physical locations for redundancy.

4. Store full backups of your data and metadata on a separate system than the machine that contains the raw data.

2. Simplify backup procedures: one of the most important tasks when managing big data is to have a backup. This is because it can be lost or corrupted, and in this age of increased cyber-security threats, your data needs to be backed up safely. The easiest way to do this is to use a cloud storage provider.

3. Implement high data security measures: Data security has become a major concern for many businesses and individuals. Big data breaches can lead to identity theft, loss of trade secrets, and much more. If a company has sensitive information, it should be encrypted and stored on an isolated drive.

Encryption ensures that only the person with the decryption key has access to the big data. The main difference between cloud-based security and physical security is that with the first, data is not stored on one's own device, but on a cloud-based server. This means less storage capacity for one's work and more dependence on the service provider.