Some companies rely on assumptions or guesswork when making critical business decisions, while others seek to leverage valuable insights from the right data sets. It’s a no-brainer that the latter approach, i.e., fact-based decision-making, is the key to remaining competitive and finding success in today’s digitized age. But for that, it’s crucial to have access to accurate, relevant, and timely data.
And about that…
The 2020 Global State of Enterprise Analytics report revealed that 97% of real-time enterprise decisions are “data-deprived.” This statistic is quite shocking given the fact that most companies believe that data insights are critical to their business growth. Even if we take the findings in the report with a pinch of salt, it’s apparent that many organizations struggle to utilize data analytics effectively.
Now, if you own a company or are responsible for the strategic planning of an organization, it’s important that you figure out which side of the spectrum your business falls into. Does your enterprise gather data for business intelligence (BI) initiatives? What data processes do you have in place to break data silos? And — most importantly — is your data generating insights that will help your business grow?
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Leveraging Insights for Business Growth
Data and analytics can provide unprecedented value to organizations. But shifting to a data-driven model requires a company to create a robust data management strategy. An enterprise must be able to extract raw information from different data sources to get a “bird’s eye view” of the business. While data silos aren’t always obvious, they can be a bottleneck to the company’s BI objectives, restricting decision-makers from seeing the big picture.
The business data comes from various sources, including enterprise resource planning software, customer relationship management suite, and web applications. Moreover, the data is available in different formats and can be structured, semi-structured, or unstructured.
For enterprises, some common types of data sources include:
- COBOL
- XML/JSON
- EDI formats
- ADO.Net Metadata Collection
- List FTP Directory Contents
Roughly 80% of enterprise data from these data sources is unstructured or semi-structured, for instance, in JSON format.
Data-to-insight Journey
To generate insights from data, a company must design a BI framework to combine data from different sources, integrate it, and transform it to be stored in a database. Doing that manually is virtually an impossible task. An automated and efficient extract, transform and load (ETL) process is necessary to collect data, clean and validate it, and consolidate it to a repository to derive accurate and timely business insights.
For instance, a company stores data in a relational database management system, say SAP HANA. It can use a data integration tool with a SAP HANA connector to retrieve data, perform different transformations — like aggregate, merge, normalize, etc. — and send it to the relevant data pipeline. This way, data trapped in silos is converted into actionable intelligence, providing meaningful insights for informed decision-making.
During the ETL process, the data must be stripped of its different formats, cleaned, validated, and harmonized into clean and structured sets of information. It’s crucial to ensure the highest possible data quality, which is only possible through automating data processes. A company must invest in a modern data integration tool to create automated and efficient data pipelines and ensure a seamless data-to-insights journey.
Not an Option, But a Necessity
Generating insights from data has become essential for companies to gain a competitive edge. It’s no longer an option but a necessity to survive in today’s competitive business landscape. Here are some ways information extracted from data can give a strategic advantage:
Improve operational efficiency: Data insights can enable companies to identify opportunities and weaknesses in operating activities to streamline processes. It enables them to identify the well-performing areas as well as the ones that require improvement.
A better understanding of the customer base: A detailed knowledge of customer behavior and purchasing patterns allows businesses to reach a more targeted audience. It helps them optimize their advertising and marketing campaigns to maximize ROI.
Product improvement: Data insights facilitate product teams in progress measurement and understand why consumers purchase and use the product. It allows them to make informed choices about adding capabilities or upgrading functionality.
Real-World Data Analytics Examples
Amazon, one of the leading and most successful companies in the world, depends on data analytics to gain actionable insights. The retailing giant collects information from various data points to understand their customer needs. The buying recommendations make it seem like the e-commerce platform’s algorithm knows you better than yourself. All thanks to the analytics and BI tools underpinned by efficient data management.
Another great example would be Netflix. The popular streaming platform has been a data-driven company since its inception. Its recommendation algorithm is powered by the massive volume of data collected by millions of users. The data model supported by insights positions the company as the leading streaming entertainment service and competes successfully with large streaming networks like Disney+, HBO, and Hulu.
What’s the Catch?
Even with the availability of modern BI tools, analysts in most companies may require several days to answer ad-hoc questions and unlock the latest insights — a timeframe that is considered too long in today’s fast-paced business world.
Furthermore, in most organizations, access to analytics is often confined to a few executives and management employees. According to a report, only 14% of companies make data widely accessible to employees. These factors can impede business growth.
To thrive in the modern generation of analytics, a company must develop a comprehensive data management strategy and enable every person to access relevant data to make informed decisions. It will make your company more agile and help data-driven decisions optimize new business strategies.
Shaping Your Analytics Success Story
Like Amazon and Netflix, your company can also leverage insights for business growth. Cultivating your analytics success story will start with gathering your company’s data analytics and reporting requirements. The next step will be designing a comprehensive data management plan, primarily focusing on breaking data silos, and integrating and transforming data to derive meaningful insights to support fact-based decision-making.
Choosing the right ETL tool can play an instrumental role (no pun intended) in the success of your company’s BI initiatives. So, you may consider buying the latest enterprise-grade solution that supports legacy and modern data sources. Furthermore, you must be able to automate your company’s routine data integration tasks to generate insights that can help propel the business forward and achieve growth milestones.
