After businesses have realized the value of big data, data generation has been a forefront agenda. However, generating large quantities of data comes with its own challenges. Utilizing data in an effective and economic manner to drive value has become the new challenge that has confronted businesses.
Our previous blogs highlighted how businesses are leveraging data-driven insights to improve decision-making. Agile decisions that are supported through robust data insights are the new normal. Business users are being empowered through self-service analytics, helping them partake in modern business transformation.
A data driven culture and mindset is spurring business culture transformation. One of the main driving forces behind this change is the generation and realization of benefits that can be obtained from Big data. As appropriately said by Gartner Research’ Peter Sondergaard: “Information is the oil of the 21st century, and analytics is the combustion engine”
Big data can be a huge boon to decision-makers if used effectively. But, what exactly is it? Why is it important to businesses?
“Information is the oil of the 21st century, and analytics is the combustion engine”
by Peter Sondergaard
What is big data?
Put simply, big data is a field that deals with large-scale data sets. They typically contain a high volume of data and are generated with high velocity. Structured and unstructured data require different techniques for analysis. Structured data may consist of in-house numerical data sets maintained by companies, ordered sales data, etc. whereas unstructured data may include emails, videos, audio etc.
Big data has numerous use cases. The most popular benefit is the improvement in the quality of decision-making. Another benefit is identifying hidden insight that can’t be seen through regular methods. Big data has also provided customers with superior insight into consumer behavior, market expectations, and market trends.
But, big data comes with its own challenges. Often times the large breadth of data collected is sensitive and includes heightened risks of cyber security threats. This sensitive information that is gathered also raises challenges around governance and compliance. Moreover, companies leveraging big data are aiming to transform decision-making and insights into a data-driven culture. But, when working with big data, it is often discovery that delays the identification and analysis process. Having some information about the data you wish to find or work with is very useful in this case. Generating a suitable profile of the data can help with speed and accuracy as well. This is where Metadata comes into handy.
What is Metadata?
Metadata is data about your data. There are different kinds of metadata. According to NISO,
- Descriptive metadata is useful in the discovery process, giving information about title, creator etc.
- Structural metadata provides information on the orientation, relationship and versions
- Administrative metadata includes information about filetype. It is typically external.
Metadata needs to be defined and managed according to the organizations’ data governance policies. Costs and storage are important factors to consider. Metadata management tools like data catalogs are often able to assist data stakeholders in their governance and initiatives. A data catalog can connect and utilize the metadata to help generate insight on date of creation, date of usage, user information, ownership, definition etc. Let’s take a look at a sample dashboard depicting the useful information that metadata can help to achieve.
So how does metadata catalog help manage your big data?
Metadata is the key to activating big data. Metadata helps make better use of big data by providing a robust profile of its characteristics. Apart from aiding in the discovery process, it heightens consistency and ensures that high quality data is easily found and maintained. Metadata is also the bread and butter that drives analytics and insight whether it be through Machine learning, Artificial intelligence or any other techniques. Only is big data is linked and identified through Metadata, can it be effectively utilized.
But, metadata itself need to be defined and maintained in order for it to be able to activate our Big data. This brings into consideration: Metadata management – a staple of any data governance initiative. This in turn raises questions about best practices that need to be considered.
Let’s dive into 5 strategies for effective metadata management:
- Adopt a metadata strategy in accordance with broad-based company strategy and data and financial goals
- Clearly define breadth, depth, scope and usability of metadata
- Use metadata management tools to effectively utilize metadata
- Leverage Artificial intelligence to maximize insight and usability
- Ensure robust security and compliance infrastructure
It’s clear that metadata has a transformative ability to help manage big data if defined and leveraged effectively. Tools like data catalogs that utilize metadata to provide a complete inventory of all data assets, data profile, and linkages can greatly boost utility as well. Now that we’ve highlighted how metadata can help achieve our big data goals. Let’s take another step towards our digital transformation goals in our journey to business intelligence.
