Top 7 Data Mining Techniques to Grow Your Business in 2021

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Data is available in a surplus amount in today’s digital world. For a business, you can get input from websites, mobiles, social media, and other channels. The important thing that companies need to recognize is how to extract useful information from all the details. To get your answers, you need to understand data mining and what it is all about.

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How Startups Can Leverage Data Mining To Fuel Growth

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Who else other than startups and small business will vouch on the power of knowledge. It is the creation of knowledge and ability to implement it that emboldens an entrepreneur to launch a startup. While starting a new small venture is easy, sustaining it as a business is the difficult part. Studies show that at least 60% of startups run out of gas within the very first year of operations. Financial constraints notwithstanding, it is the inability of entrepreneurs to reinvent themselves that is responsible for such a phenomenal failure rate. Here the tricky question is why are new small businesses unable to rediscover themselves? A close look at the success stories on new ventures will reveal that they are able to derive new insights from their existing data. And how do they achieve this feat? The answer is data mining.

What is data mining

It is essentially the process of identifying patterns within a large database of information using certain software and tools. By applying this process to their data startups can gain valuable insights into customer behavior, sales, customer experience, market position vis a vie competitors and a host of other aspects of business. In turn this readily available knowledge helps them foresee challenges and make timely decisions. Data mining also involves data collection, warehousing and data processing using the latest statistical and algorithm based tools.

data mining architecture

Preparing for data mining

Now that you are aware of the potential of data mining in ensuring success of small enterprises, you would obviously be eager to take the plunge. But wait. Jumping into the bandwagon of data mining will be of little consequence unless you are fully prepared to do so and have a fair understanding of the sub processes involved in it. To make the most of this potent tool you need to have an in-depth understanding of your target market and competitors. Here’s what you need to do before testing the waters:

Strategize: Make a long term strategy of what exactly you want to achieve by taking up data mining as a business practice.

Keep customers at the helm: Identify your target audience and carve out a plan on how exactly you want to enhance your customer experience.

Assess your competition: A thorough analysis of your competitors is a must to understand their strengths and weaknesses.

Develop strong messaging: It is a rather meticulous exercise of identifying what exactly your potential buyers are looking for and then coming up with a message to engage them.

Acquire technology: You need to have access to the technology that allows you to enhance your customer experience and engagement. If analytics and related tools are too expensive to acquire, find a vendor that can procure the necessary systems in a cost effective manner.

Once you are fully prepared to leverage data mining, your next step should be to identify the technique that suits your individual needs. As data mining equips you with new knowledge derived from your data, you should have a clear understanding of what exactly you want to achieve from newly acquired insights. This approach also helps you to pinpoint the data mining technique that would work for you. Now, let’s look at the different techniques available and what do they offer in terms of process and outcomes.

Data mining techniques

Data mining techniques

Industries across the board rely on four different types of data mining techniques to drive growth:

This technique is ideally suited for simple data mining processes as it collects data from predetermined sources. In terms of architecture, non-coupling data mining does not rely on resources offered by database. To process the data it uses algorithms designed to meet certain goals. One big plus of this technique is that it allows you to store the results in any file format. On the flip side, its inability to leverage the functionalities of database makes it a weak data mining technique.

pros and cons

Loose Coupling Data Mining

Loose coupling data mining is considered to be a very efficient and agile technique. This is simply because of its ability to collect and process data from any segment of your database. This technique can also use the different functionalities offered by database and data warehouse to augment processing. You can store the results in your file system or within the database. As loose coupling data mining can retrieve data from any resource, it adds to its agility in delivering results. This technique also proves to be cost-effective as data localization is not required. However, this advantage can become a stumbling block as it can delay the query response time.

Loose Coupling Data Mining
Loose Coupling Data Mining

Semi-tight Coupling Data Mining

This technique is primarily meant for identifying patterns within the data being mined. Semi-tight coupling method can make the most of warehouse system components and functionalities to speed up mining activities like indexing and aggregation. It also allows you to store certain results within the database. As results are stored within the database or data warehouse, it can lead to latency in complex query resolution.

Semi-tight Coupling Data Mining

Tight Coupling Data Mining

In tight coupling data mining database or data warehouse itself acts as an information retrieval tool. The architecture is complex, but offers a lot of flexibility and scalability in terms of driving results. However, this technique calls for expensive infrastructure and data security provisions. As data is copied over, complex query resolution is becomes speedy and accurate. Being a complex system, it proves to be an expensive proposition for startups.

Tight Coupling Data Mining

Validation and Enrichment

Applying the right mining technique as per your individual requirements and budget can make every startup a sustainable business. However, to really leverage the power of data mining you need be hands on with data validation and enrichment processes. These processes not only help you measure the performance of your data mining techniques, but also give you the leeway to tweak your mining model thereby enabling you to drive the desired results. If you are not an authority on data validation services, outsourcing these to to a specialized vendor is your best bet. Data validation measures data mining on three attributes – accuracy, reliability and usefulness.

Accuracy: Checking data for missing or approximate values and ensuring that data has not altered in the processing phase.

Reliability: Measuring the performance of data mining vis a vie changing data sets and making sure that the patterns are uniform irrespective of technical information provided in the data.

Usefulness: Applying matrices to measure the usefulness of the results.

Validation and Enrichment

Data Enrichment

It is the process of improving and enhancing raw data into a valuable business commodity that enables entrepreneurs to make well informed decisions. There are a host of service providers who can deliver bespoke data enrichment services to you.

The 6 main attributes of data enrichment are:

Data Fusion: Integration of data sets with same real world representation in a precise and useful manner.

Name Recognition: Identifying people, businesses, cities and entities with similar names and classifying them into categories.

Data Disambiguation: Standardizing textual data extracted from different systems.

Segmentation: Grouping data into segments depending of similar features.

Data Imputation: Estimating and imputing values for missing or inconsistent data.

Data Categorization: Labeling data according to preset categories.

Data mining: Helping startups succeed

Take a look at the top ten startups of 2018; whether it is Rubrik, Ripple, Bird or Lyft, you will find one thing common among them and that is, these ventures never stopped deriving new insights from their data. This simply means that for a startup to be successful it needs to treat data as a valuable commodity and create strategies to turn this raw information into valuable insights. While earlier it was difficult for small businesses to do so given the constraints of meager resources, now there is no dearth of techniques and services that make data mining accessible to startups. In the present scenario you can outsources data mining services, data validation services and data enrichment services at affordable prices.

Data mining services not only enable small businesses to build customer relation strategies that work, but also bolster their sales and marketing. In addition to this, startups are able to allocate their resources in a better way and mitigate risks.

Hope this write up gives you an idea of the efficacy of data mining in sustaining your fledgling business and helps you in applying this technique.