In one form or another, we all hear it said more and more these days: Data is the new currency. It’s the coinage of our digital age, minted by every user, hoarded by every organization. Even the nomenclature of this data-world suggests a precious commodity, as we “warehouse” the products of our “data mining.” While this literalist imagery is useful to sketch a vague idea of what these data systems do, it requires a bit more reflection to understand how a data warehouse helps with data mining.
Data mining is, accurate to its name, an industrial process that extracts valuable data from mountains of mixed information. With warehouses the metaphors break down somewhat. Warehouses are used not only for storing the valuable “data-ore” resulting from the mining process, but also for storing and sorting the mountain itself. This article will reveal warehouses as important utilities, part of a complex procedure for generating insight, and show why they’re an indispensable part of the business intelligence economy.

Defining mining

At its core, data mining is a series of procedures that allow ad hoc exploration of complex data. This undirected nature of data mining is important — unlike other procedures for understanding data, data mining does not start with a hypothesis or predetermined line of investigation. Instead, it uses automated solutions to detect patterns in collections of data that are too large or messy for humans to easily interpret. That means that most of the technological complexity of the data mining process is not in the mining itself — which is achieved by AI models or discriminative algorithms — but rather in the processing of raw data and in the interpretations of patterns discovered by the mining process.

Data mining algorithms

Most of the sorting systems used in data mining are well-established approaches, machine learning tools that have been deployed for decades in a variety of sciences and industries. Clustering algorithms, regression analysis, support vector machines, or neural networks are used to pull patterns from raw data. The wide variety of possible approaches includes both supervised and unsupervised learning algorithms (algorithms that take labeled or unlabeled data respectively.) This is why data mining relies on thoughtful preparation of its analysis targets: in order to have the flexibility to trawl data with a variety of AI tools, the input data must be prepared for consumption by many different machine learning systems, each with their own requirements.

Data mining inputs

The primary reason an organization employs data mining, as opposed to more directed analysis, is an overabundance of data. If the data is too much for humans to sort, data mining algorithms step up to finish the task. This complexity of analysis might seem at odds with the input sensitivity of a mixed bag of AI tools. In fact, it only means that the hard work of data mining happens in the data preparation phase.

In order to supply useful data to machine learning tools, a business intelligence or data analysis team will need a tool to massage their untamed data into a useful context: synchronizing diverse data types, unifying timestamps, cleaning data anomalies, and identifying and fixing corruption. This step requires a strong understanding of the diversity of data sources, as well as an idea of which algorithms will be best suited to parse the sanitized product.

Understanding the outputs

Once the data has been organized and algorithms have detected patterns in it, the data mining system calls human intuition back into the process. Results first have to be interpreted — junk correlations separated from unexpected insight. When working with huge quantities of data the wheat can be hard to separate from the chaff — the data lookups, table merges, or error sanitation put load on whatever data system operates them. Computing brute-force patterns can be expensive as it is; no one wants to run up the costs in the data preparation stage.

The data mining sequence can sidestep some of these costs by recording correlations for later review. What seems like a useless autocorrelation now may turn out to be significant later, given more data and new events. Useful connections can generate new types of data, which in turn can be used by financial forecasters, marketing teams, or business planners to create new models, generating instant insights from historical data.

Whatever its end goal, a data mining process wants to record all of its outputs. This record should be well indexed for easy lookup and recall; if conclusions are too messy to easily review, then there is hardly any point in having recognized the pattern in the first place!

Bringing in the warehouse

Data mining requires a great deal of information storage and processing, operations that can be optimally performed by a data warehouse: a data storage system designed for query performance with large and varied data sets. Before going further, it can be helpful to review the essentials of a data warehouse — but we will discuss below several specific advantages to a data warehouse that yield high utility in the mining process.

Unity of data

A data warehouse marshals a potentially messy assortment of data sources to a single location, while standardizing formats, normalizing timestamps, and rooting out anomalous/erroneous data. This makes a data warehouse the perfect tool to prepare data for machine learning algorithms. Although human attention might still be necessary, a proprietary warehouse will usually integrate most of the data sanitation necessary to the mining process out of the box. Data warehouse tools usually also incorporate connections to machine learning systems, making integration of inputs and analysis easy for the analyst.

Responsiveness

Many data mining algorithms require precise data inputs to achieve their results. This can mean highly expensive queries to the data repository. Data warehouses are also well suited to address these issues, especially compared to relational databases. Accommodating industrial-strength queries unblocks potential bottlenecks from the start, and translates to faster turnaround on data insights.

Historicity

One of the primary ways a data warehouse achieves its responsiveness and data unification is with temporally-organized data series. Uniting data on a common timeline makes historical trends much easier to find and gives unrelated data sets a common context. In this way, data warehouses can improve pattern generation and, again, reduce analysis turnaround time along with computational costs.

Preservation of insights

An important feature of a data warehouse is its storage stability. Warehouses not only store and organize disordered data, but can also retain the output of data trawling by mining tools. By unifying the source of data mining and the resulting output, a data warehouse provides a full-spectrum view of the mining process, empowering later reviews that are looking to understand whatever patterns may have been recognized during mining.

Does data mining require a data warehouse?

Data mining refers to the entire process of deploying robotic tools to find connections and correlations in unexamined data. The specific tools chosen vary widely in their usefulness to different types of problems; the ones used will depend heavily on the data that is being examined. However, in general, if data mining is called for, the parameters of the problem confronting the analyst are too complex for them to dig through themselves.

A data warehouse brings specific advantages to a process of this complexity, making mammoth quantities of convoluted data easier to manage, reducing the difficulty of algorithmic analysis, and simplifying later review of the results. These advantages are significant and applicable to amost any type of data mining operation. Furthermore, data warehouses have query schedulers and queues that are designed to prevent the entire system from locking up during particularly computationally intensive queries that may be performed during data mining activities. This is of course extremely important to ensure that regular business activities are not impacted by data mining efforts.

Therefore, in general, data warehouses are a highly preferable partner in the data mining process.

Learning more about data warehouses

Data warehouses have utility beyond data mining. More directed analysis processes will also find advantage in using a warehouse for storage, in cases where data is large, historically organized, or built into data stacks with a high requirement for responsiveness. You can learn more about some of these processes in our other learning center articles:

• Data Warehouses versus Big Data
• Data Warehouse Best Practices
• Business Intelligence and Data Warehouses