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Uncover Hidden Customer Insights with Qualitative and Quantitative Data Analytics

Data professionals strongly advocate quantitative when it comes to qualitative and quantitative data analytics. It’s the numbers that business leaders most often tend to look for. But those numbers are just like soup without salt if they lack context. Have you ever wondered what if you add an element of qualitative figures to those numbers and how would they help in getting better customer insights?

Though often subjective, Qualitative Data is rich and consists of in-depth information. Recently several breakthroughs and software specifically designed for qualitative data management have been made. This has two-fold benefits:

  1. Greatly reduced technical sophistication
  2. Ease laborious tasks. Thus, making the process relatively easier

Wondering how to incorporate and integrate both the aspects — an amalgam of qualitative and quantitative data analytics into customer insights? Let’s dive into the ‘Whys’ and ‘Hows’ of combining both qualitative and quantitative analytics!

The Paradigm Shift: Why is Quantitative and Qualitative Data Analytics in customer insights more Important than ever?

Organizations are collecting data in various capacities from both internal to external sources. To understand customer insights and behaviour comprehensively, businesses need to consider a balance between insights from qualitative and quantitative data analytics.

It’s a fact that collecting qualitative and quantitative data analytics cannot be overlooked when you drive efforts to maximise sales and improve customer experience, customer insights, and business growth. While numbers and ratings from researchers, customer insights, and even competitors are easy to analyze, businesses require insightful qualitative and quantitative data analytics to have a complete picture of their customer insights and demographics.

Let’s look at the challenges which businesses face while identifying relationships between Quantitative and Qualitative data analytics to decode customer insights and the status.

Challenges in Identifying Relationships between Quantitative and Qualitative Data analytics

Using data mining techniques aimlessly doesn’t generate customer insights and may blur true relationships hidden in the data. Instead, knowledge of consumer behaviour should guide analysis by identifying important variables. This isn’t always easy to achieve, and that’s why below are some obvious yet recurring issues that you might encounter while conflating quantitative and qualitative data analytics practice.

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Siloed Data Sources

Oftentimes, the legacy enterprise infrastructure for managing data may not be efficient. Traditional data warehouses often lead to disparate silos of data which may prevent you from getting a holistic overview of consumer insights and data. With information about customers now coming in from hundreds of places- from internal to external systems

Combining Structured and Unstructured Data

Accumulating data doesn’t mean just assembling documents, sheets, etc. You should be able to connect different types of data, both structured and unstructured, in meaningful ways. Traditional data mining techniques might not enable you to extract and discover deeper and more subtle patterns in consumer insights and behaviour.

No Contextualized Discoveries

To be able to predict consumer behaviour in both qualitative and quantitative data analytics, your analytical builds should relay relevant facts and contextualized answers to specific questions, rather than a broad search result with lots of irrelevant information.

Unidentifiable Macro Relationships

To reveal the macro relationships and dynamics in business, qualitative and quantitative data analytics is important and also to get a fundamental understanding of consumer insights. Traditional data modeling techniques may restrain you from identifying relationships at a macro level.

Quantitative and qualitative data analytics provides access to a huge range of content ready to use for customer insights, together with the tools to integrate proprietary data. The deeper the data, the more confidence you can have in your business decisions and customer insights. We at Rawcubes offer the industry’s most comprehensive market data, including real-time and historical data.

Rawcube’s quantitative and qualitative data analytics capabilities provide pre-defined or customized analytics calculations to customers as a fully managed service.

How can Businesses Leverage Knowledge Graphs to Transform Data Management

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According to Michael Atkin, Director of EKG foundation, knowledge graphs have changed the game in terms of helping companies move away from relational databases and leverage the power of natural language processing, semantic comprehension, and machine learning to improve the rightful utilization of data.

Knowledge graphs are essential for establishing AI-powered semantic applications that can help you discover facts from your content, data, and organizational knowledge which would otherwise go ignored. By fundamentally understanding the way all data relates throughout the organization, graphs offer an added dimension of context which informs everything from initial data discovery to flexible analytics, he states.

Let’s understand how enterprise knowledge graphs (EKGs), and graph technology, in general, can help businesses manage their data effectively. We’ll also look at various real-time use-cases of graph technology.

Let’s dive in!

Breaking the Ice with Unified Data Silos

 

With the omnipresence of organizational silos and independent LOBs, the legacy enterprise infrastructure for managing data is not efficient. The data silos, when consolidated with external standards for glossaries, entity & relationship, databases, and metadata repositories, lead to incongruent data. Because of this, it becomes difficult to align the silos. As a result, organizations end up with data that is hard to access, blend, analyze and use, impeding application development, data science, analytics, process automation, reporting, and compliance.

With enterprise knowledge graphs, you acquire data that is integrated and linked rather than data that is siloed. As a result, organizations become more efficient because ontologies remain standardized and reusable.

Once the data silos are unified, you need to understand where it fits into your ecosystem. Let’s glance through some of the industry use cases of graph technology.

Business Use-Case of Knowledge Graphs

 

Cisco: Real-time Graph Analysis of documents saved over 4 million employee hours. To assign metadata to the large collection of Cisco’s historical documents, they transformed pdf and Microsoft Word file types into a Latent Dirichlet Allocation (LDA) format so the documents could be clustered by large data platforms.

Lyft: According to Tamika Tannis, 90% of Data Scientists are using knowledge graphs for routine tasks. KGs have increased productivity for their entire data science division by 30%

AstraZeneca: Joseph Roemer, Senior Director, IT insights & Analytics stated that they used graph algorithms to determine journey types and patterns of patients and then identify others having close or similar behavior.

Caterpillar: Employed graph technology to create a logical form of knowledge. The team created a data architecture that ingests text via an open-source NLP toolkit, which uses Python to combine sentences into strings, correct boundaries, and eliminate noise in the text. Data can also be imported from both SAP ERP and non-SAP ERP systems.

Hästens: Leveraged knowledge graphs to automate and streamline the management of requests for its product catalog, drastically cutting the time between order and delivery.

NASA: Chief Knowledge Architect, David Meza says that using graph technologies helped them eliminate some issues from the Apollo project, saving almost 2 years of work and 1 Million dollars of taxpayer funds.

Corporates can reap rewards by switching from traditional relational databases to knowledge graphs: capturing the knowledge from data and relations between the concepts. Since it focuses on concepts rather than precise data formats, semantic modeling avoids the problem of hard-coded premises. Even when data travels across organizational boundaries, users spontaneously understand what it represents, enabling effective reuse across systems and processes.

Thus, understanding the transition from relational database architecture to a semantic and ontology-oriented framework like knowledge graphs is important.

The Transition from Columns To Context with Knowledge Graphs

 

The majority of corporate data is kept in a relational structure and is accessible using the widely used but rigid SQL language, whereas in knowledge graph data is stored in a manner that emphasizes on terms and building their relationships.

In order to benefit from knowledge graphs, organizations must invest in new infrastructure, data must be transformed and new skills must be learned, says Amit Weitzner, co-founder Timbr.ai. You need to identify the infrastructure that is ideal for your organizational construct. An ideal infrastructure, however, can be divided into 3 components: graph storage, graph query API, and storage mutator.

Graph Storage: On-prem relational data store as the fundamental database, on top of which you can implement a node & edge store which enables you to perform basic CRUD operations on nodes (entities) and edges (relationships), instead of dealing with conventional relational schema.

Graph Query API: In the knowledge graph API module, in addition to CRUD endpoints for nodes and edges, provide a graph query endpoint. You may traverse the network using a graph query by defining a path, which is a series of edge types and data filters that starts at a certain node and returns the traversed subgraph in a structured format. A recursive interface is included in the graph query API.

Storage Mutator: You need to constantly import data to the graph storage and propagate these mutations downstream for which you can build a storage mutator.

Thus, knowledge graphs are crucial for a substantial shift towards a data-centric approach. The shift must involve rethinking the existing software architecture and making it more data-driven and declarative

Final Thoughts: Intersection Of Graphs And Machine Learning

 

Knowledge graphs have revolutionized the way data is stored and processed by corporations. They have a deep impact on machine learning and AI training, eventually speeding up the learning process for machines.

DataBlaze builds data intelligence and provides seamless data integration through knowledge graphs with a comprehensive multi-cloud data processing platform to drive agility and efficiency.

Our elite knowledge graph-driven machine learning data management reduces the time your data SMEs – operations, marketing, analysts & scientists take, to make informed and retroactive business decisions.

There is more! With DataBlaze, you use NLP, AI, ML, and simple drag & drop features to empower everyone – right from data analysts to business operators, to discover and build datasets for advanced analytics. Optimize your data management life cycle with DataBlaze software.

Linking the Right Dots with Knowledge Graph

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Over the years, there has been a massive surge in the amount and type of data that is being generated from different sources. Most of this data is unstructured and complex. This poses a major challenge for businesses who want to get quick and meaningful insights from this complex, a disorganized, yet valuable pool of data.

For a long time, traditional/relational databases have been ideal for storing structured data in tables with columns that are of a particular type with several rows consisting of defined types of information. Due to its rigid structure, developers and applications have to strictly structure their data to be used in their applications. The indication to other rows and tables is done by making reference to primary key attributes through foreign key columns. The calculation of JOINs is done at the time of querying by mapping primary and foreign keys of all the rows in the connected tables. The more the relations, the more the number of JOINs required. Hence, the operations involve heavy computing and memory power, not to forget the huge overhead cost. To limit the heavy cost associated with this process, the data is denormalized to limit the number of JOINs required. However, this results in the loss of data integrity of the relational database.

These limitations have led one to shift their focus to graph databases which are a viable alternative for linking disparate data sources. Graph databases make it easy for programmers, users, and machines to interpret the data and derive insights through a simple representation of entities and links between data. This in-depth understanding is critical for machine learning technologies that use context-based machine learning for reasoning and drawing inferences. Graph databases allow one to construct simple and sophisticated models that look quite similar to the problem domain by interconnecting nodes and relationships. It allows the data to stay in the same way as it appears in origin or in the real world making it easy for the user to query and view the data from any perspective and support various use cases.

Hence, one can see from the above that to enable quick and intuitive business decisions, it is essential that the data be stored in a way that one can access it readily without dealing with various complexities that come with traditional databases. Moreover, businesses today are relying heavily on information from various digital channels to make informed and intelligent decisions for future growth and performance of their business. Traditional databases proved futile when it came to challenges like ad-hoc addition of new data structures, acquisition of new companies, and combining unstructured data with structured information.

DataBlaze: Knowledge Graph-based Data Platform

DataBlaze is a knowledge graph-based data platform for faster Data Discovery Solutions. Once data is ingested, DataBlaze uses machine learning (ML) and artificial intelligence (AI) models to automatically discover the terms and relationships from the incoming data, and a context/business identification is given to the data, i.e. knowledge about employee or customer by merging knowledge and relationship. The automatic mapping of patterns with the graph structure saves time and manual efforts of a subject matter expert (SME). The SME only needs to provide the labels and approve relationships. Hence, businesses can save time and get standard functionality off the shelf.

DataBlaze is flexible enough to store knowledge graph into a wide variety of graph databases like neo4j, janusgraph etc. The incoming data (source data) can be stored in variety of data storing platforms currently available in the market. It may be on premise/cloudDB.

DataBlaze has the capability to store or transport data into Object Store, HDFS, BigQuery. In future releases, DataBlaze will also be able to store source data/raw data into GRAPH DB as well.

So, don’t you think it’s about time that you contacted us for a first-hand demo of DataBlaze?