Today’s leading companies have one thing in common: They use big data and AI-assisted decision-making in every aspect of their business. With so much consumer activity happening online, from research to shopping to product reviews, businesses can use data from every touchpoint along the customer journey to better understand their customers and reveal important insights. When companies utilize data effectively, they base their decisions on real customer behaviors rather than on hunches, trends, or the status quo.
The amount of data available to businesses is massive, hence the term “big data.” It refers to data that is so large and complex and is collected so fast that it would be impossible to keep up with via traditional means. Therefore, companies often turn to the experts, such as Darwoft’s data teams, to utilize artificial intelligence (AI) to analyze and gain insights from large datasets.
AI-assisted decision-making has become essential for many businesses, and it comes with many benefits. AI models and algorithms systematically extract useful information from large quantities of data quickly so that stakeholders can make timely decisions. Applications like predictive modeling can be used to provide a glimpse into the future. Plus, AI is less prone to human error and cognitive bias.
Data is the foundation of AI-assisted decision-making. The models and algorithms designed to analyze data are only as reliable as the data itself. Infrastructure and processes must be put into place to organize, analyze and apply data with accuracy, consistency, and integrity. Most companies openly recognize the value of data-driven decision-making, but they lack the expertise to put such systems in place.
Fortunately, Darwoft’s team of skilled data engineers, data analysts, and data scientists can help companies use data to optimize every aspect of their businesses, edging out the competition along the way.
Why is data important?
Data gives businesses the information they need to make better decisions and give them a competitive edge in a fast-paced market. Data provides the facts on how all aspects of the business are performing, and it represents the true voice of the customer. With this information, companies can make evidence-based decisions and predict future outcomes with a higher degree of certainty.
Leading companies use data-driven solutions to improve every aspect of their business, from operations to marketing to customer service. Data can help with fraud detection in finance, inform customer segmentation for e-commerce, and be used in predictive modeling for sales forecasting in retail. It’s also a key component in machine learning for business, an important differentiator for enterprise companies that want to maintain their position in the marketplace.
Bottom line: Data is your key to success. If you want to operate more efficiently, grow your market share, and increase your revenue, then you have to be a data-driven company.
Who makes up a Darwoft data team?
Darwoft’s data teams include four important team members:
The role of a data engineer is to design, build, and maintain the systems and infrastructure necessary for collecting, storing, processing, and analyzing large volumes of data. Their goal is to make data accessible and usable by all company stakeholders, not just by technical staff.
Data analysts have the important task of beginning to make sense of the data. Their role is to examine and interpret complex data sets to uncover patterns, trends, and meaningful insights that can help inform business decisions.
Data scientists take the analysis one step further by using advanced analytical and statistical techniques to extract insights from data and build predictive models and algorithms. These tools enable companies to make and implement timely data-driven decisions and optimize their activities at the highest level.
Business Experts have a deep understanding of the given industry, company, and its products or services. They play a critical role in asking the right questions and guiding engineers, analysts, and scientists toward the data that will help a company reach its goals.
How does Darwoft’s process enable data-driven decision-making?
In order to help our clients become data-driven companies, Darwoft uses a multi-step process to organize, analyze and apply data. In essence, this process is akin to a well-oiled machine, in which each task works like a gear fitting perfectly with the next one.
Let’s walk through this process:
The first task is to import large, assorted data files from multiple sources and move them to a new destination. This process is called data ingestion and is performed by Darwoft’s highly skilled team of data engineers. To accomplish this, engineers use a variety of processes and technologies to build efficient data pipelines. A data pipeline is a set of data processing elements connected in a series in which the output of one element is the input of the next one, thereby transporting data from one place to another.
Darwoft’s engineers employ an ETL strategy when building data pipelines. ETL stands for Extract, Transform and Load, and refers to the order in which these actions take place. Once the data is extracted from its original sources, it undergoes a data transformation process in which engineers transform it into the required format and structure for its new destination. The benefit of an ETL pipeline is that the data is already formatted and qualified once it reaches a repository, making it better prepared for analysis.
When transforming data, engineers use a variety of data cleaning techniques to fix or remove incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. Data cleaning is a critical task, as there are many opportunities for data to be duplicated or mislabeled when combining multiple data sources. Data engineers must have a strong understanding of data governance and data management practices, as they are responsible for ensuring the accuracy, consistency, and security of the data they work with.
Once the data has been cleaned, it’s time to take the first step toward reforming the data into useful and meaningful information. Engineers use various data integration strategies to bring together data gathered from different systems and create a single, unified view for company stakeholders.
One key task in data engineering is identifying and establishing the source of truth for each data element as well as ensuring that this source is maintained and updated in a consistent and reliable manner. “Source of truth” refers to a single, authoritative data source that is considered to be the most reliable and accurate. For example, you may determine that customers who have purchased within the last two years are considered “current customers,” while customers whose most recent purchases happened before that time frame are considered “lapsed customers.”
This source of truth serves as the primary reference point for all other systems and applications that need to access or use the data. The concept of a source of truth is important because it ensures consistency and accuracy across an organization's data landscape. Without a clear source of truth, different departments or systems might have conflicting versions of the same data, leading to confusion, errors, and inefficiencies.
The transformed data is then loaded into a data warehouse, an information storage system that aggregates structured and processed data. It’s specifically designed to support data analysis and other business intelligence activities. (You may have also heard the term “data lake,” which is a repository for raw, unstructured data. In this case, the data is aggregated first and then transformed into a unified state for analysis.)
With the transformed data now secure in the data warehouse, the baton is passed to the data analysts. Data analysts use a range of tools and technologies to gather insights within the data set, including SQL (structured language query), data visualization, statistical programming languages (like R and Python), machine learning, and even good old-fashioned spreadsheets.
Data visualization is a graphic or visual representation of data, such as charts, infographics, and animation. These visual representations are designed to quickly communicate their findings from large and complex data sets. Data visualization tools are particularly important when it comes to reporting insights to stakeholders, as they help decision-makers process the information and reach decisions faster.
It’s important that data analysts have a clear understanding of stakeholder needs so that the insights presented are aligned with overall business objectives. Skilled data analysts are able to communicate their insights effectively to ensure that the information is understood and can be used to inform future decisions.
At this point, data scientists use AI tools to gain additional insights from the data, thus creating a true AI-assisted decision-making process.
One of the most important AI tools used in data science is machine learning, which gives computers the ability to learn without explicitly being programmed. Machine learning algorithms are used to build predictive models, which use statistics to predict future outcomes that adjust to changing variables. These models give stakeholders the ability to test a decision in an exploratory environment before implementing it in real life. With predictive models, companies can better understand how their customers might respond to a new service offering or how much they’ll pay for a product. Machine learning also helps to improve data quality by identifying and correcting errors, and it helps prevent fraud by detecting unusual behavior and patterns.
AI-Assisted Decision-Making for Your Business
To become a data-driven business, you need a team of data experts behind you.
Darwoft’s highly-functional data teams consist of the top experts in their field working with the most advanced technologies to deliver extraordinary value at a fast pace.
Our teams of engineers, analysts, and scientists are well-versed in multiple programming languages, methodologies, and practices. Our customers know that their data will be handled with the highest degree of technical expertise to ensure accuracy and quality.
Your data holds the key to meeting your next business goal, and we’re here to help you get there. To learn more, contact our team at email@example.com