Covid-19 has pushed businesses to embrace digital transformation to help drive innovation and thrive in the ever-changing technology landscape.
Businesses are moving faster than ever before, and the only fuel that can power this speed is data. Fortunately, the entire data industry has been innovating at a rapid pace. New architectures, new cloud platforms, and new technologies have given rise to the modern data stack, opening new opportunities for businesses brave enough to be early adopters.
We have collated the top ten trends in data management and analytics that will come to the fore in 2022.
A data warehouse is a unified data repository for storing large amounts of information from multiple sources within an organization. While valuable for its time, a centralized data warehouse in an on-premises world could take months to build. Highly curated data can obscure more valuable granular insights. Costs could be high.
The term data lake was coined in 2010, with the promise of speeding access to granular data and
lowering costs. Unfortunately, these became known as data swamps, too slow to be usable.
As businesses want to rely on data further, it is becoming increasingly important to analyze structured as well as unstructured data. Data Lakes became popular due to their ability to easily capture unstructured data such as log files, images, videos, and more. However, to support BI tools, this data then needed to be transformed into structured data via ETL processes. We are now seeing the rise of new Lake House approaches that apply a metadata layer on top of data lakes which allow business applications and ML applications to interact more seamlessly with structured data and unstructured data directly.
The data lakehouse combines the best of both a data warehouse and a data lake, offering converged
workloads for data science and analytics use cases.
Another significant trend that we are seeing is the rise of data mesh. A new data architecture paradigm becoming increasingly popular as forward-thinking companies embrace semi-structured and unstructured data, and seek to democratize access to it, is the data mesh. a Data mesh is a type of data platform architecture that supports distributed, domain-specific data consumers and views “data-as-a-product”, with each domain handling their own pipelines. This approach can help your data platform scale over time and is worth exploring.
The development of cloud computing technology processing has seen widespread adoption with AWS dominating the sector. A survey conducted shows that the global cloud services industry is projected to be over more than $623 billion by 2023, with a growing compound annual growth rate (CAGR) of 18%.
Navigating these new concepts is the foundation of your future digital success, and the time to embrace them is now.
AWS recently launched Sagemaker Canvas making it incredibly easy for business users to create their machine learning models without writing a single line of code. Sagemaker Canvas works by cleaning and combining the data and creating hundreds of models. Users can import data with just one click from various data sources. This platform will enable business analysts and data analysts to work completely independently, without any reliance on engineers or data scientists.
Microsoft also has Azure Machine Learning Designer. Over 2022 we are going to see further development in no code machine learning tools. AI and ML will continue to be commoditized, making it very easy for businesses to grow using these techniques. The drag and drop methods make the interface user-friendly. Tasks like classification, regression, statistical models are supported by Azure Machine Learning. It also works well for data teams as it has the data labeling feature as the central place for teams to work.
Today, it’s not enough to have a transactional-based enterprise system that just automates processes through code. Most large enterprise software providers are embedding machine learning in every aspect of their systems. From allowing users to configure model parameters to adding analytics behind the scenes, we will see AI & ML permeate software systems. We’re not just seeing it in customer analytics, but also supply chain optimization, order management systems, PLM, and more. It uses the knowledge from the existing data to make decisions and assessments, independently. The data visualization and analytics are placed in the user interface of the application. The intuitive nature of these embedded analytics makes it popular among users.
A data-driven culture is currently amongst the hottest business intelligence trends.
While organizations have always been interested in their numbers and figures, the extent of data use is exercised at a higher level within a data-driven culture. A data-driven culture should not be only interpreted as merely following numbers. It should encourage the advancement of data interpretation skills, deriving insights from data and critical thinking, which enables businesses to base their decisions on reliable data. The idea is to build a cultural framework that helps all members of the organization to collaborate and contribute to moving data at the center of decision making.
In the new data landscape, the demand for data analysts and analytics engineers has been rapidly growing thanks to the rise of the modern data stack that brings agility, scale, and the power of low-code platforms.
A data analyst pairs their technical knowledge with an understanding of the business, a skill that has been sorely lacking in many junior data scientists.
Enterprises are going to use data in every vertical to make better decisions and improve efficiency. We will start seeing data analysts in every vertical from sales & marketing to merchandising and warehousing. Thanks to the plethora of data analytics tools available, it will become easy for these analysts to get up and running and produce results quickly. Furthermore, they will help the company make decisions faster, improving time to value drastically. Ensuring your organization can tap the capabilities a data analyst can deliver will be crucial to your success.
A survey of data scientists by CrowdFlower revealed, 76% of data scientists say that data preparation is the worst part of their job. But the fact remains that efficient, accurate business decisions can only be made with clean data. Data preparation helps in fixing errors quickly by helping catch errors before processing. It also helps in producing top-quality data by cleaning and reformatting datasets and eventually making timely, efficient, and better business decisions.
Additionally, as data processes move to the cloud, data preparation moves with it for even greater benefits, such as superior scalability, accelerated data usage, and enhanced collaboration.
As businesses become more data-driven, the importance of data quality will increase. Good data quality is the foundation of any data initiative, from business intelligence to machine learning. Fortunately, there are many tools and services today that can help you cleanse data quickly. Cleansing ranges from format validation (email, phone, dates, etc.) to complete address standardization. Furthermore, companies want to get accurate information on their customers, so we are seeing a wave of deduplication initiatives across companies.
Originally focused on analysis, data preparation has evolved to address a much broader set of use cases and can be used by a wider range of users.
While it improves personal productivity for anyone who uses it, it has evolved into a business tool that promotes collaboration between IT professionals, data experts, and business users.
Data governance is a collection of policies, processes, standards, and metrics that ensure the quality and security of the data used across an organization. An elaborate data governance strategy is essential for any organization that works with big data. Data governance is critical to capturing value through analytics, digital, and other transformative opportunities. Data governance has served as a foundational component of data strategies for years.
Data governance is a complex but critical practice, and most enterprises have encountered
difficulty in mastering all its requirements. While many companies struggle to get it right, every company can succeed by shifting its mindset from thinking of data governance as frameworks and policies to embedding it strategically into the way the organization works every day.
With an ever-increasing application landscape, there is a surge in the volume of data being produced by businesses. To prevent data leaks and attacks, proper governance structures need to be put in place. Per our comments under “Data-Driven Culture”, there will be a surge in the data analyst role and as the title suggests, the need for data access by teams is only going to increase. Setting up the right access and authorization framework for data access can prevent outages due to human error.
Data lineage is the process of understanding, recording, and visualizing data as it goes through all the transformations it underwent along the way. It helps understand the data life cycle and is one of the most crucial pieces of information from a metadata management perspective.
In line with data security and ensuring compliance with new data regulations, understanding the flow of data across the organization is paramount. One needs to understand what type of data lies in which systems to effectively triage issues that may arise from customer complaints to business continuity reports. Also, as enterprises begin to replace legacy systems, it will be vital that all integrations with such legacy systems are replaced with a new system without any disruption. Appropriate data discovery can mitigate downtime in such scenarios.
Finally, to take advantage of all the insights produced by various teams and systems, data needs to be activated to be effective for the business. This is done through e-mail campaigns, advertising, website personalization, and more. Businesses are moving from producing macro reports on a weekly/monthly basis to producing insights per second that are then automatically acted on by core customer communication systems in real-time to produce immediate results.
Effective data activation requires strong collaboration driven by evangelism from company leaders. It is also important to find trusted partners for both data and analytics services whose capabilities complement yours. Enlisting third-party data and analytics solution providers to help navigate change and implement new approaches often provides a cheaper and faster direct path than building those capabilities internally.
The ability to target a user with the right offer, at the right time, in the right channel is only made possible through the rise in data and compute capabilities. Machine learning models make it possible to predict such behavior, whereas real-time integration capabilities make it easy to act on such predictions immediately. It enables rapid decision-making, breaks down data silos, and future-proofs your business. This sort of personalization leads to significantly higher customer lifetime value. Imagine getting an e-mail for an offer on new running shoes just about the time your existing ones are wearing out. Today we can make such predictions thanks to the transactional data being collected over the years.
Further, to achieve agility and speed interactions between systems, effective response to the powerful market forces and integration of data in real-time is essential.
To ensure an economical and efficient transfer of data, great customer engagement, better visibility, and insight into your business and superior customer service, real-time data integration in business is a must.
Tying all of the above are data platforms. While each data platform is unique, they generally encompass data management, analytics, and activation in some form. At its core, a data platform is a central repository for all data, handling the collection, cleansing, transformation, and application of data to generate business insights. They are highly scalable and can perform real-time integrations with many systems; this makes them easy to insert in most product ecosystems. We are seeing Customer Data Platforms become a growing category in retail and automotive. Companies like Treasure Data and Amperity have extraordinary data capabilities that have led to enterprise success. While data platforms have been traditionally focused on B2C companies, we are seeing B2B platforms emerge as well.
A key trend that needs to be highlighted is that data-first companies like Uber, LinkedIn, and Facebook increasingly view data platforms as “products”, with dedicated engineering, product, and operational teams working to maintain and optimize them.
Being data-driven is no longer ideal; it is an expectation in the modern business world. Organizations have accelerated their digital transformation journey; now they understand that they need to be mindful of how they integrate and manage enterprise data that is distributed, still easily accessible, trusted, and governed.
We, at Systems Plus, help businesses leverage the benefits of the cloud and harness the power of data-driven business intelligence. Speak to us to find out how we can leverage the cloud to help you unlock business value and overcome the challenges of data silos, data latency, and more!
If you’re ready to start your data journey and keep up with the 2022 trends, reach out to us.