Category Archives: Big Data

Big Data, Data Warehousing and Data Mining

Michael Koploy from Software Advice posed recently a question about plain definitions of some basic Business Intelligence concepts – Big Data, Data Warehousing and Data Mining. Although question seems to be quite simple, it is mind provoking due to changes that BI is experiencing to during last year or two. New developments in this area force us to look again at these concepts. Here is my view on these 3 topics:

Big Data

Simple definition:

The concept of the big data is not new, although it gained popularity during recent years. It describes all the data available to organizations and that includes structured and unstructured data. It is characterized by its large volume, variety and velocity, which makes it challenging to analyze. Until recently organizations tended to limit amount of information by putting breaks and structure through governance and architecture. Too much information was considered bad thing, due to limited capacity of systems and capabilities to process this information.

How it is changing:

The old saying – ‘garbage in – garbage out’ is not true anymore. Organizations realized that among the garbage there might be lot of valuable information that could be monetized. This could be done directly or indirectly and used not only to generate revenues but also to gain competitive advantage. The value of information might not be correctly estimated at the time of its creation or during its initial intended use. Value is often defined by its context – to paraphrase – “the value is in the eye of the beholder”, and it is also time variant. Traditional BI was dealing primarily with the structured data, as it was easier to work with and get results quickly. The rest was mostly ignored or treated as necessary evil. The problem however is that unstructured data constitutes around 80 to 85% of data within the organization, or floating over there in the web, and it could be in one or another way related to the business. Social networks like Facebook, Twitter, blogs, discussions, memos, emails and so on are equal sources of potentially useful information. The winners from losers are separated by ability to see the value where others do not, and ability to use it.


Data Warehousing

Simple definition

Traditionally data warehousing is a process of consolidating and aggregating information from various sources within the organization, and used for historical analysis and reporting. The outputs from the analysis are used for operational, tactical or strategic planning. Before the data could be used for these purposes however it has to go through process of cleanup, standardization, normalization, integration and so on. Once stored in Data Warehouse it could be aggregated, and correlated to find answers to typical business questions.

How it is changing

Once data is in Data Warehouse it becomes relatively non-volatile, time variant, representing subject oriented historical value of data. Here is the problem in the new world – the process of standardization and structuring of the data often strips the most valuable part – intrinsic relationships between data, that might not be visible at the time when the structuring rules are established. Usually Data Warehouses are created with specific goals, and these goals might be changing relatively quickly. Adjusting Data Warehouse to fit these new goals might be as painful as turning a large ship in narrow fiord. In the light of Big Data, the whole concept will have to be reevaluated.


Data Mining

Simple Definition

In short it is discovery of true meaning of data from large datasets that integrates structured and unstructured data. These datasets might come from data warehouses or from any other data sources. Data mining helps to answer specific business questions that might be unique and might not have predefined processing paths.

How it is changing

Data mining is building on available data and thus closely related to the above discussed two terms. Since these terms are changing, so it is the data mining concept. The organizations need to employ innovative techniques like statistical tools, semantic analysis, neural networks, artificial intelligence and so on, to extract information from combination of both structured and unstructured data in order to gain knowledge. This single step is what separates ‘wheat from the chaff’, winners from losers – it is the ‘holy grail’ of Business Intelligence.

Majority prefers ‘big data’ on premises rather than in the cloud

According to recent AIIM’s survey, the ‘big data’ adoption is going to double to 17% during next 12 months. This penetration is going to increase further to about 60% within next 3 years. The survey confirms the old truth – the need for holistic view of the data – over 61% of respondents would like to see integrated information, coming from both – structured and unstructured sources. Classification of unstructured data seems to be ongoing problem, with over 70% of organizations finding that it is easier to find information on the web, rather than on their own internal networks. Although search techniques and tools improved over the years, it seems that the adoption of new technologies is pretty slow. Another big factor playing large role in this is the poor data governance.  With regards to analysis of the data, the requirements don’t seem to be very sophisticated, indicating that organizations still struggle with strategy how to effectively use the ‘big data’. Most respondents would be satisfied simply with basic pattern analysis, keyword correlation, incident prediction and fraud prevention. This fact seems to be confirmed by lack of answer to an important question. When asked about a ‘killer application’ for their business area, over 88% of respondents said that it would make a big difference in their business, but when asked what it would be, majority declined to answer.

Another interesting fact from the report is that most of respondents seem to confuse search with data analytics. Although there are some overlaps between the two, the former is about returning results matching selection criteria, while the latter about processing of the data to return answers about specific business question.

Lastly, not so good news for cloud vendors, over 88% of respondents would prefer on-premise big data storage and analysis, rather than SaaS solutions. This seems to be related to perception of poor data protection on externally hosted applications (although only 64% of respondents explicitly stated this). Majority considers the business insights as organization’s intellectual property. Cloud providers will have to work harder to convince the market, as data security question will continue to be the primary barrier to cloud adoption.