The Reality Of Working With Data

There is much talk about the commercialization of Big Data. Many understand its benefits, but there is a lack of awareness on how it actually happens. Some organizations join the Big Data game with misconceptions that it is a simple, easy, and quick implementation and have no real strategy in place to derive value from the data. The truth is that there is a disconnect between Big Data expectations and reality.

Working with data is a complex process that needs time, effort, and a proper strategy. Unless organizations have the necessary skills in-house, they would need to have the right partner or vendor that has the data engineering and data transforming solutions in place to turn raw data into a high-quality data product–one that is both accessible and consumable.

Strategy First

Before embarking on Big Data investments, the first step an organization needs to do is to set a data strategy. This refers to the overall vision, as well as the definitive action steps, which serve as the platform for an organization to harness data-dependent or data-related capabilities. Data architects and engineers need to have clear and specific objectives to achieve the organization’s data goals. A misconception when it comes to data investments is that an organization just needs to get high-quality information at a rapid pace and this will immediately translate to improving decisions, solving problems, and gaining valuable insights. This is simply not true. What is needed is a detailed road map for investing in assets such as technology, tools, and data sets.

Challenges in Working with Data Sets

Data sets, a collection of data or group of records, come with built-in issues. This is the reality of working with data. It is imperative for organizations to know this in order to understand why the process takes some time. Below are the challenges that the entire industry faces:

Bigger Data Sets

Working with data requires processing bigger data sets and large volumes of data, which cannot be handled by traditional data processing applications. Big data infrastructure is needed when volume increases to GBs or TBs and above. Therefore, engineering for scalability becomes a big issue.

Inconsistent Data Sets

Organizations generate data based on their needs, resources, and technical capabilities, which means that they may not be consistent; hence, the collected data is not clean and standardized. They need to be cleaned before they are ready for industry consumption –a process that can take time.

Multiple Data Formats

Another issue is handling multiple data formats (csv, json, xml). Without fail, when a company sells data the receiving party will require it in a format that isn’t the same as the native storage format. Data vendors must provide facilities that allow for the consumption of data products in multiple formats corresponding to client requirements.

Data Frequency

Data frequency ranges from one-time batch files to real time data streams. Depending on the buyers’ use case and infrastructure, the frequency of consumption can vary vastly. Data vendors must ensure they can facilitate these varying requirements to maximize the number of potential buyers for their data products.

Data Quality Analysis

Organizations need to perform quality analysis regularly to ensure that data products remain of a high standard. Every data set that is intended to be transacted must be evaluated according to industry standards before they are made accessible for sale.

Data Delivery

On-time automatic data delivery is another issue faced when transacting data and is often overlooked by organizations due to human resource constraints. Data becomes irrelevant and devoid of commercial use once the need for it has passed. Therefore, making the data available when the client needs it is of utmost importance.

The Solution

To solve these issues for organizations who are looking to transact data, DataStreamX has developed a data lake architecture that takes pressure away from data vendors by handling the ingestion, transformation and automated data delivery to clients. Our data lake holds large volumes of raw data in varying formats. It is a simple idea that turns into a rather complex system of processing modules, interconnected with pipelines, and backed with databases.

The data lake architecture has many features that make it a viable solution to the issues presented earlier. In terms of scalability, it can perform distributed data processing on huge and growing data sets to achieve horizontal scalability. The data lake can also work well with data sets of different formats such as logs, XML, multimedia, sensor data, binary, csv, json, and gives the flexibility for these data sets to interact together. All these different formats can also be stored and processed together in a data lake architecture.

Another advantage of implementing a data lake is that two or more data sets can be combined to gather better insight and analysis. Secondary data sets can be used, together with the primary one, in order to analyze relationships and make predictions. An example is analyzing two different location data sets to identify better marketing strategies.

In addition, it provides organizations the flexibility of consume data in multiple ways such as via API calls, SDKs, or via automated data pipelines. Organizations need not be fixed to consuming data in a restricted manner, and can choose the best consumption method for the use case at hand. For example, the data can be consumed for different use cases like analytics, data visualization, and machine learning, which can help in management decision making.

Conclusion

In conclusion, organizations should enter the Data Economy with more knowledge than what is advertised in marketing materials. The reality of preparing Big Data for transaction is that it involves some work like data cleaning and transformation before a data product can be created and monetized. But transacting data does not need to be complex or exhaustive to your resources. It can be made simple if we first understand the data and take the necessary steps when creating the data products. Lastly, work with partners who can shoulder the burden and complexity involved when transacting to third parties.