Big data strategies to helo you get the most out of your data.
Big data consists of extremely large sets of structured, semi-structured, and unstructured information that originates from a multitude of sources. These may include transaction records, demographic data, social media content, text, images, audio, and video — a diversity of material that conventional management and analysis methods may fail to deal with.
Yet, we must deal with it, particularly in the business arena — because these vast volumes of diverse information usually contain insights and patterns that individuals and organizations in all walks of life can use. And the uses of big data can be as varied as the nature of the information itself. Analysis of enterprise data may yield results that enable businesses to improve their working methods, product and service offerings, communications, customer interactions, health care delivery, and a range of other things.
In this article, we’ll be looking at some of the big data strategies you can use today.
Data Mining
Data mining is the process of delving deeply into large data sets, with the intent to uncover anomalies, patterns, and correlations that can inform decision making or predict outcomes. By employing a wide range of data mining techniques, you can use this information to increase business revenues, cut costs, improve customer relationships, and reduce risk.
Three scientific disciplines lie at the heart of data mining:
- Statistics – The numerical study of relationships within data sets.
- Artificial Intelligence (AI) – The development of software and machines that display elements of human-like intelligence.
- Machine Learning (ML) – Complex mathematical processes or algorithms that can learn from data to improve system performance or make predictions.
Data mining is one of the foundation stones of data analytics, which enables enterprises across various industries and disciplines to filter out the noise in their data, understand what is most relevant, and uncover connections within millions or even billions of records.
For example, in the manufacturing industry, product makers can use data mining and predictive analytics to predict the rate of wear on their production assets and anticipate maintenance needs, leading to increases in uptime and productivity. Data mining also assists in aligning supply chain strategies with demand forecasts and maintaining quality standards.
In the finance sector, data mining helps financial services companies gain a more holistic view of market risks, detect fraud more quickly, and manage their regulatory compliance obligations.
Data mining helps educators access student data, predict achievement levels, and use predictive and behavioral analysis to identify individuals or groups of students who may need extra attention or intervention strategies to keep them on course. These same techniques help health care providers assess diagnostic data and the response of patients to their treatment protocols.
Insights gained from the data mining of customer databases helps retail and service organizations keep in step with consumer demand, improve customer relationships, and optimize their marketing campaigns.
Big Data Strategies: Marketing
With the expansion of information sources through social networks, online interactions, and the data streaming from devices and machines of the Internet of Things (IoT), sales and service industries are becoming increasingly reliant on big data marketing — the application of analysis and large data sets to sales and promotional activities.
As a professional in this sector, you need accurate data to know which marketing channels are best. Reliable data also gives you the relevant insights that enable you to make smart decisions. The key is to use methods and technology that enable you to collect, analyze, and display or visualize the data and results in a meaningful and relevant way to your business.
In this context, big data marketing allows you to spot trends, patterns, and behaviors that your competitors might miss. Big data marketing tools can help you uncover insights hidden in your advertising, analytics, sales, and customer relationship management (CRM) platforms. Used in conjunction with machine learning, big data marketing systems can also provide prescriptive courses of action.
For content marketers, big data marketing tools with machine learning features can provide information and recommendations for creating material that appeals to the right audience and deliver it at the time that gives them the most value. The analysis of massive amounts of unstructured data enables psychological segmentation, a process whereby machine learning algorithms can target customers with relevant personality traits, delivering content, and advertising that’s tailored to their preferences. This approach can dramatically increase conversion rates.
By merging information from multiple data sources, big data marketing enables you to create more personalized experiences for consumers and reach out to them across every channel they use to interact with your brand.
Master Data Management (MDM)
Many businesses today have hundreds of separate applications and systems with data that crosses organizational divisions or departments. This dispersal can cause enterprise data to become fragmented, duplicated, and / or out of date. The confusion most commonly applies to critical data assets such as product data, asset data, customer data, or location data. And when this fragmentation occurs, answering even the most basic but critical questions about any type of performance metric for the business becomes problematic.
Master data management or MDM addresses businesses’ need to keep crucial information accurate and relevant and consistently manage this data and keep data definitions up to date as more information streams into the enterprise.
We can define master data as the core data within the enterprise that describes objects around which business revolves. Typically, master data changes infrequently and can include reference data necessary for the operation of the business. While it isn’t transactional in nature, master data does describe transactions and exists in four general domains: Customers, Products, Locations, and Others.
The Customer domain includes customer, employee, and salesperson sub-domains.
Product, part, store, and asset are sub-domains of the Products domain.
Within the Locations domain, there are office location and geographic division sub-domains.
The Other domain includes sub-domains for things like contract, warranty, and license.
While master data is typically a small portion of all enterprise data from a volume perspective, it includes some of the most complex data — and the most valuable to manage and maintain. And because multiple applications use master data, an error in that data in one place can cause errors in all the applications that use it.
Master Data Management is not simply a technological problem, in many cases requiring fundamental changes to business process for its successful implementation. Nonetheless, there’s a growing market in big data tools for MDM. These tools take care of the cleaning and standardization of master data, the creation of master data models, the merging and remerging of data over time, management of master data hierarchies, and auditing.
Big Data Strategies: Training
There continues to be a skills gap in the big data sector, with analytics and data management talent lacking in many organizations and salary levels for big data professionals among the most lucrative in the IT industry.
Not surprisingly then, a number of big data training schemes are in operation, whose aim is to imbue candidates with the skills they need to store, manage, process, and analyze massive amounts of structured and unstructured data. Besides these core skills, courses, and certification programs for big data training generally teach candidates how to:
- Select the correct big data stores for disparate data sets.
- Process large data sets to extract value, using industry-standard tools like Hadoop and Spark.
- Query large data sets in near real-time with specialist tools such as Pig and Hive.
- Plan and implement a big data strategy for various kinds of organizations.
