Data is the foundation of today's corporate environment. Data drives strategic decision-making, uncovers new income sources, and exposes hidden waste producers that are negatively impacting your bottom line, regardless of your industry—finance, healthcare, or omnichannel retail.
However, none of those things can be accomplished by data alone. A tech stack that links all pertinent data sources must be put together. Regretfully, it's difficult to choose the platforms and data analytics solutions that will best position your company.
In order to choose platforms and solutions that meet your company objectives, we go over assessment criteria, important features, and more in this post.
Selecting the Appropriate Data Solution
The wide range of use cases, applications, and industries that Big Data may be applied to makes it challenging to provide a clear set of selection criteria. Here are some ideas to help you focus:
Decide on objectives.Even while it's simple to become overwhelmed by the potential of Big Data analytics tools, establishing your program's primary objectives and creating a solid plan are far more crucial than the tools themselves. With your data strategy, what goals do you intend to accomplish? Build your toolset around these primary objectives by focusing on a small number of business issues or opportunities that have the most potential for improvement, such as real-time asset monitoring or a better grasp of your clients' needs.
Examine use cases that are specific to your industry.Investigate whether analytical tools, platforms, and capabilities are being used by others in your sector to address issues or generate opportunities. For instance, whereas a financial services company would be more focused on fraud detection, retailers might examine how other businesses employ sentiment analysis or AI recommendation engines to enhance the consumer experience.
Consider the end-user.Everyone in the organization, from the C-suite to your customer-facing teams, must be involved in the plan you deploy if you want to get the most out of Big Data. Think about how analytics relates to various positions in your company. Which users require decision-supporting simpler solutions? Do you require tools related to marketing or sales?
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Are you capable of data science
Key Features and Types of Data Analytic Tools
Which tools are employed in big data analytics? Although they tend to fall into a few major classes, the tools form a wide category:
Platforms for customer data (CDPs)
Similar to a customer relationship management (CRM) platform, a customer data platform (CDP) collects customer data that may be utilized to enhance workflows or promote goods. On the other hand, CDPs go above and beyond.
CDPs collect data from anonymous website users and maintain and track data both online and offline, whereas CRMs only collect data from deliberate interactions (unless manually input) such as communications history, website visits, and purchase habits. They deal with a wide range of data kinds from various data sources.
Essential Skills:
- gives a 360-degree perspective of the client
- links several data sources, including first-, second-, and third-party data.
- combines client information from all linked systems.
- enhances marketing campaign targeting
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Tools for Business Intelligence (BI)
Businesses can view and comprehend data with the aid of today's business intelligence (BI) solutions. Gartner states that BI tools fall into three primary categories:
Data discovery, ad hoc reporting, simulation models, performance management, and other sophisticated analysis features are made possible by online analytical processing, or OLAP.
Dashboards, reports, and visualizations are some of the ways that information delivery provides insights.
In addition to managing metadata, BI integration offers a development environment to help you implement your plan.
The objective of BI tools is to assist firms in becoming data-driven using procedures including data mining, predictive modeling, and natural language processing, even if these platforms are somewhat varied. Trends like data visualization and embedded analytics are also starting to emerge, allowing non-technical people to access insights that previously required IT support.
Citizen data scientists may use their expertise in data, analytics, and business to address important new business challenges thanks to these easily available platforms.
Essential Skills:
- Visualization of data
- Modeling predictions
- Information mining
- Predicting
- Reporting that is automated
- Dashboards that can be customized
- Connectivity with other platforms and data sources
- Management of data quality
- Processing natural language (NLP)
- Management of performance
- Ad hoc evaluation
- Models for simulations
- Making a budget
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Analytical Tools for Customers
The whole analytics process, from planning to insight creation, is managed by customer analytics tools. Pre-built data models for forecasting, propensity to purchase, and a range of statistical analysis techniques are typically included in customer analytic platforms in order to better understand consumer behavior and enhance goods, services, and experiences.
Remember that the tools your sales or marketing team may employ are not as advanced as customer analytics systems. Even if pre-built models make these platforms easier for non-technical people to use, creating and executing new models or gaining complete insight into the customer journey still requires highly skilled data scientists.
Essential Skills:
- Fine-grained segmentation
- Insights on customer happiness
- Modeling statistics
- Metrics for acquisition, retention, and attrition
- Analytics for text
A relatively new class of enterprise-grade software called Digital Experience Platforms (DXP) aims to maximize the customer experience at each touchpoint. Although DXPs and customer experience management (CXM) platforms have similarities, DXPs are primarily concerned with optimizing workflows and organizing and customizing content for users across a variety of platforms, including as digital assistants, virtual reality, and the Internet of Things (IoT). Giving advertisers strategic control over branding and content presentation is the primary objective here.
What Qualities Should a Data Analytics Platform Have
According to a BI Survey research, laggards are more likely to choose a full-stack platform, while leading organizations are more inclined to invest in specific tools or create custom solutions that fit the needed capabilities. For companies with general use cases, few data projects, and a desire for rapid implementation and training, all-in-one solutions could be a better alternative.
It's also important to remember that every tier of your stack must take the make vs. purchase debate into account. To make sure you cover everything, you must examine data processing, artificial intelligence, machine learning, storage, predictive models, and integration platforms separately.
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Expert Assistance and Services
What qualities are you looking for in a service provider? Certain solutions, namely SaaS products, are built on business models that profit from being beneficial (retaining customers). Think about if you want to collaborate with a partner who provides practical assistance and direction on maximizing the benefits of their product.
Other options, like as self-hosted solutions and open-source platforms (Hadoop, Spark, Apache), are typically more self-directed. In this case, you buy from a vendor and use internal knowledge, frequently asked questions, and how-to guides to help you through the process. Remember, there can be a trade-off. SaaS technologies have some restrictions and might not support every data type or integration you would like to employ in your tech stack, even though they frequently offer far greater support.
Options for Data Storage: Self-Hosting vs. SaaS
Another important factor is deciding between on-premises hosting and a SaaS option. Cloud-based SaaS systems are run by a third-party provider who takes care of platform IT infrastructure management, including data backup, storage, and security.
SaaS hosting is an affordable, scalable option that can interface with other systems and shorten time-to-market. Using a SaaS platform for business intelligence (BI) or customer analytics has the advantage of making it simpler for non-technical individuals to acquire and comprehend findings without the assistance of an analyst or IT.
Self-hosting, on the other hand, entails operating a platform on your personal server. Compared to SaaS systems, self-hosting allows for greater customisation, but it also requires more work to get started. A self-hosted solution is probably a better alternative for bigger enterprises that need bespoke solutions since it gives you more flexibility over data integration, reporting tools, and automation that is tailored to your specific requirements.