Learning to effectively manage large datasets is key to optimizing your data analytics. Using data clusters can help optimize your data management. It involves grouping data points with similar characteristics into the same cluster to speed up processing time and improve analysis. Consider starting an analysis project on customer spending habits by gender in different regions. Your time on the project would be maximized if you’re able to use statistical techniques to automatically organize the data into logical groups prior to analyzing.
How do data clusters work? Data clustering allows you to partition large volumes of structured and unstructured data/observations into logical groupings. One way it does this is by analyzing all of the data in the data warehouse and comparing each data point with clusters created. You rely upon the clustering algorithms to sort and cluster the data in a logical way. In a perfect world, all data points in the same group should be highly similar in nature, while data points in different groups should be dissimilar. There are several different models and algorithms that guide the clustering process. Here are a few:
- Hierarchical Method: This method creates separate successive clusters using specific criteria.
- Partitioning Method: This method works to discover all clusters at once and then partitions them.
- Density-Based Model: Members of this cluster are grouped together based on observation density and similarity.
What benefits do they have for data management?
Data clustering and analysis can revolutionize your data management process. For starters, since clustering is done by algorithm, there’s a strong possibility you can discover previously unknown correlations within the data that could help you approach a business challenge from a new perspective. When you’re managing large data sets, observation can only take you so far.
When it comes to data mining, or extracting data, you can use data clustering as a stand-alone tool to get insight into data distribution or to hone in on specific clusters on which you’d like to perform additional analysis. You can also use to it in business intelligence to restructure customers, organize pending projects, and to support numerous other applications. Clustering helps make data mining more efficient by minimizing the number of scans required to query data and lessening the load for server.
What systems work well with clusters? Microsoft SQL Server Analysis Services and Azure Analysis Services both work well with clusters. Microsoft has a clustering algorithm similar to some of the algorithms we previously mentioned, that integrate with these services perfectly. It also has a sequence clustering algorithm that combines sequence analysis with clustering. You can use it to uncover cases that contain similar paths in a sequence within Azure and Microsoft SQL datasets.
Data clustering is a surefire strategy to help your management team handle complex big data. The quality of an organization’s data analytics is strongly related to the direction its leadership takes. Invest time to discuss data clustering with your IT team. You may find that it’s already being implemented within your systems, or you may discover its time for you to put it into place.
Dobler Consulting is a full spectrum database service with 10 years of experiencing helping companies strategize and execute workload management solutions. For more information about Dobler Consulting and how we can help you integrate data clustering into your database strategy, visit www.DoblerConsulting.com or call us at +1 (813) 322-3240 (US) /+1 (416) 646-0651 (Canada).