I have recently joined the fitness sports club. Since the day of my registration, my browser has been sending me advertisements related to sportswear. I wonder how google understood that I have joined the fitness club. It’s so amazing, that my browser answers all my queries without even me asking for it. It’s like someone is laying a carpet that has all your wishes which you can fulfill with a single click. Industries are getting piled up with loads and loads of data. Today each interaction of the customer is getting recorded to create a customer-centric value chain, which in turn results in a more specific business goal. This huge data needs data curation.
What is Big Data?
Big data, the term itself is self-sufficient to provide its definition. Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity. It relates to extremely large data sets that may be analyzed computationally to disclose patterns, trends, and relations, especially linking to human behavior and interactions.
Volume, Velocity, and Variety are the three crucial properties of big data. The Big Data problem gets tri-folded when a wider ‘Variety’ of data needs to be accommodated for competitive analysis but your infrastructure is in no position to support it.
What is Data Curation?
If we talk about Big Data as a huge reservoir of information in the form of data sets, then data curation is the process of organizing and integrating the collected data from various sources stored in this big data. Data curation includes “all the processes needed for principled and controlled data creation, maintenance, and management, together with the capacity to add value to data”. It is active and ongoing management of data throughout its lifecycle right from the time its created until it is stored for future analysis and process.
What top analytical reports say about Big Data
As concluded by various top research companies like Forbes, IBM, Chicago Analytics Group, we have highlighted what executives think about big data.
3.1 trillion a year is the cost to the US economy for the poor data quality
91% of the companies believe that poor data waste revenue
79% of the executives are afraid of falling back in competition and losing the cutting edge if the big data is not incorporated in the business
83% of the enterprises are already chasing big data to gain the competitive advantage
10% reduction in the overall cost of business has been reported after implementing big data
$1 billion per year on customer retention is saved by Netflix using big data.
Decoding and organizing Big Data
Data curation decodes huge stockpiles of big data and organizes it for further use. This data is segregated in below properties;
- Volume – The amount of generated and stored data
- Variety – The type and characteristics of the data
- Velocity – The pace at which data is produced and processed
Data curation processes help in;
- Effective Machine Learning(ML)
Training data for Machine Learning(ML) and Artificial Intelligence(AI) are prepared using data curation. ML plays a key role in understanding consumer space. Although we have technologies like AI, Deep Learning, humans need to intervene and further curate algorithmic behavior towards effective learning for the creation of intelligent self-service processes to provide business insights.
- Data swamps handling
A data swamp is a badly designed, inadequately documented, or poorly maintained data lake. The data lake is a pool of raw data stored in the repository for users to analyze and retrieve information as and when needed. Data curation enables the segregation of data and preserves good data in the lake. Thus, it can be pragmatic to escape from data swamps.
- Ensuring Data Quality
Data Curators clean and undertake actions to ensure the preservation and retention of the long-term authoritative nature of digital items. This further provides intrinsic value in educating the audiences and speeding up innovation.
Phases of Data Curation
Data curation is the process of turning independent structured and semi-structured data into collated data sets for analytics. Data Curation includes data validation, archiving, management, preservation, recovery, and representation. 3 major phases of data curation are;
- Identifying
Data curators collect data from diverse sources. Identifying the right source of data is the job half done. Identification of data is considered as the 1st and the most crucial step in beginning with the problem statement. Identifying the right data is as important as solving the problem itself. When data identification is done the right way, a huge amount of time can be saved beating around the bushes and eventually assist in providing the optimized solution to the problem.
- Cleaning
Now that you have identified the right data sets which vary in type and formats, it’s time for cleaning the available data. These data sets may have a lot of anomalies like improper entries, missing values, formatting errors, spelling mistakes, duplicate entries, empty files, etc. Most of the data may get converted into data swamps as it’s always unclean. The most important part of data curation is cleaning the data which tri-folds the value of the data for further use.
- Transforming
Transformation is the process of converting data from one format to the other. These formatting can be to integrate data into repositories that are many times more valuable than the independent parts. The data sets are converted from the source to destination formatting which is machine ready and can be formulated by the computers or any defined hardware equipment or software program. The purpose is to exclude the redundant formats or migrate to the newer system. Example: moving doctors reports from hard copies to pdf, banking balance sheets to Tally software, etc
The job of a data curator
More the data, complex and costly becomes the data curation activity. Data curation is more concerned with the maintenance and management of metadata. Data curators not only look into how to create, manage and maintain data but also determine the best practices to work with it.
To get the curated data, a curator determines which of the data sets are most relevant and useful. Effectively presenting the data is equally important and making a balanced decision on selecting the appropriate data set is the crucial part of data curators to answer the problem statement.
Data needs to be from a trusted source. Data curation uses such intermediaries of modern taste as lists, popularity rankings, annotations, relevance feeds, comments, articles. And the upvoting or downvoting of data assets to determine their relevancy.
Conclusion
With the help of data curation, you can apply the powers of big data to a lot of varied fields such as product development, predictive maintenance, customer experience, fraud and compliance detection, machine learning, and operational efficiency. The better you are at data curation, the more efficient you can be in your business.
So take this 1st step, connect with Oriental Solutions experts, have a librarian-like curation functional expert for your big data. Reach us @ http://orientalsolutions.com/contact.php or call at 044 2498 6018
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