Tools for Analytics in the Modern Data Stack
With the influx of data recorded yearly, it is becoming increasingly difficult for businesses to make sense of such extensive information reserves. But there are tremendous benefits for businesses that can extract meaning and value from high-quality data. To this end, data science is becoming progressively integral to business growth and has the capacity to increase revenue or save a failing company.
As more businesses are waking up to the value of data analysis, new analytics tools have emerged to help collect high-quality data. Before this, data quality was mostly measured for reports delivered to clients; however, with the advancement of technology and data being a key component in machine learning, businesses are adopting a more distinct approach to analyzing data quality.
This article aims to clarify the modern data stack, highlighting the importance of analytic tools in data technology and the benefits to business growth.
What is a Modern Data Stack?
Simply put, a data stack is a set of analytic tools for data integration. As a concept, “data stack” originated from the term “technology stack:” a combination of technology tools used by software engineers to build products or services. While the latter focuses on different use cases, the modern data stack is designed to assess, manage, and store data.
The Modern Data Stack comprises a set of technologies built on cloud-based services, including more and more low-code or no-code tools to help businesses save and utilize their data. Born from the need to leverage the ever-increasing amount of data being recorded, the structure of the data stack has undergone different changes typically defined by the following:
- The growth of cloud-based data warehouses - With the advantages of cloud-based storage, there is an improvement in data security and flexibility, allowing systems to store large chunks of data at low cost.
- The migration from Extract-Transform-Load (ETL) to Extract-Load-Transform (ELT) - The move from ETL to ELT made it easier to load large amounts of data in cloud-based data warehouses in minutes. In a modern data stack, data is loaded into the cloud before being transformed using ELT tools, which in contrast, are much lighter than conventional ETL tools.
- The rise of self-service analytic tools - With self-service solutions, businesses can connect all data sources in a few clicks, creating tailor-made reports based on simplified data models.
As startups collect data early, businesses are beginning to rely on analytics to amass insights that help them grow. Likewise, by building data stacks, they can utilize available data for strategic decision-making. The modern data stack saves cost, time, and effort that would otherwise be spent designing, building, and maintaining data connectors to give data analysts and scientists access to high-quality data.
Analytic Tools in the Modern Data Stack
As previously defined, the modern data stack is a set of tools used to analyze data, discover new insights, and improve efficiency. Here are some of the kinds of cost-efficient tools for data analysis included in a modern data stack:
Consumer Data Platforms - Typically used to integrate customer data into a single database, consumer data platforms are marketing analytics tools that offer useful and strategic insights for marketing campaigns. Using platforms like Segment, businesses can grab information from different online sources to provide a complete overview of a customer. With this data, companies can predict the ideal next move of each individual customer. These platforms enable businesses to learn about customer preferences and create solutions for increasing customer retention.
Product Analytics - Mixpanel described product analytic tools as excellent for illustrating the importance of the user journey to both product managers and marketers. With a clearer view of a customer’s first experience with a platform, businesses can analyze consumer behavior and understand what made them take action on the platform. This gives product and marketing teams a detailed overview of the user journey and highlights opportunities to improve consumer value.
Engagement Platforms - With the integration of high-quality data collected via analytic tools, teams can create more targeted, personalized messages to engage a larger number of users. Iterable, Onesignal, Braze, and Customer.io are customer engagement platforms with connected technologies that help companies reach, engage, and retain users. To ensure a seamless end-to-end customer journey, customer engagement stack components must be able to provide back-and-forth interaction between providers and users in real-time.
A/B Testing - With A/B testing and experimentation, businesses can secure a reliable way to determine whether a change to a product will have the desired effects or outcome in the long run. However, designing, deploying, and implementing these tests is a complex process that may require a rare combination of technical capability and organizational engagement. Incorporating tools like Optimizely and Kameleoon can give businesses an in-depth understanding of how a product impacts key metrics across their user base by analyzing their various concurrent user experiences.
Attribution - For modern businesses, attribution tools help marketers determine what streams impact revenue or leads. Digital marketing analytics acquired through marketing attribution software like Adjust, Appsflyer, and Branch allows teams to examine and identify channels of value in which to invest time and budget. In digital marketing, attribution tools operate among marketing channels, websites, apps, and other endpoints to determine which sources drive conversion and paint a clear picture of your customer journey.
Data Warehouses - Through a columnar data store, data warehouses offer faster processing than transactional databases and allow users to quickly access relevant data elements. Whereas an on-premises (on-prem) data warehouse requires maintenance of both hardware and software, a cloud-based data warehouse has no physical hardware - It's a “software as a service” (SaaS), where businesses pay for storage space and computing power required at any given time. Internal databases and SaaS platforms like Google cloud provide storage for companies to consult when making strategic decisions.
decision-making
Business Intelligence Tools - These include technologies that transform data into practical information. The goal is to highlight areas for operational improvement or possible expansion by incorporating data visualization that improves strategic business decisions. Aside from internal data analysis, businesses utilize business intelligence from third-party platforms like Mode, Retool, and Looker to gain insights into customers, competitors, and investment leads. Ultimately, companies use business intelligence to gather, store, and analyze data and provide access to machine-learning storage to identify significant trends or patterns that assist decision-making and increase cost savings.
Why Is It Important to Integrate Your Data Tools?
Due to the misconception that data analysis is reserved for large enterprises, smaller businesses that do not embrace big data risk becoming irrelevant or uncompetitive. But data analytics has a place in companies, both large and small, and the benefits range anywhere from creating personalized sales pitches for customers to identifying and navigating risks in a business. The following are the advantages of data tool integration:
Comprehensive customer profiles - Data analytics help organizations synthesize relevant information from various channels to better understand customer behavior and journey. This creates an extensive knowledge base of customers' experiences, allowing companies to predict user behaviors and actions on a platform. By running behavioral analytics on consumer data, businesses can optimize their customers’ journeys and create a more personalized experience.
A strategic guide to business decision-making - Through A/B testing and predictive analytics, companies can visualize the effects of certain decisions on the business. When a business changes a product, analyzing sales data can determine if the results will ultimately produce the desired effects. Decision-makers can confidently choose whether or not to implement changes for the business and avoid financial losses.
Streamlined operations - Data analytics can improve operational efficiency. By collecting and examining collecting and examining data on internal operations makes it data on internal operations, it becomes easier to identify possible weak points. For example, in the demand-supply chain, where most businesses struggle with stockpiling, data analytics can help predict unforeseen hindrances that may result in production delays.
Risk identification and management - Risks are inevitable in business, but with adequate oversight, they can be managed with minimal effects on operations. For startups, data analytics is useful in estimating security risks and providing recommendations for mitigating those risks. Businesses can use analytics to build models to help cut back on losses and manage recurring issues.
Statistical models for security - Security threats are commonplace, so businesses are increasingly processing relevant data collected from analytics to help identify reasons for past security leaks. Using statistical models, businesses can simultaneously run monitoring and alerting systems that detect and flag anomalies to help teams take preventive actions.
Businesses looking to expand their digital frontiers require tools for analytics that store, access, and centralize data to improve sales. The modern data stack provides startups with the tools needed to operate in a data-driven environment, offering insights into customer engagement, competition trends, and operation management.
At Outliant, we provide data architecting solutions that allow businesses to measure KPIs, utilize machine learning, and help personalize marketing. Get in touch with us and start utilizing your data more efficiently.
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