In a world where technology integration plays a pivotal role to both create innovative solutions for business and increase market share, the demand for business intelligence and analytics is on the rise.
Prediction is at the core of all analytics and will become one of the key differentiators for companies in the SMB space. The intelligence generated by machine learning, when applied correctly to a real-world business situation, is empowering organizations and customers alike with new level of trust, customization and empathy.
SMBs cannot afford to sit on the sidelines and watch today’s technologies transform into tomorrow’s opportunities. With this in mind, companies need to ensure they are in on the ground floor of the new opportunities that these technologies hold for them. AI and Machine Learning are growing fast and innovation in the space will only increase over the coming years.
Access to insights unimaginable decades ago
The rapid technology advancement in data science and analytics now provides us with a range of solutions, large and small, that combine technologies we have always known to work in different combinations. Because of the vast amount of data that can be processed nowadays, we are also encouraged to take this data to the next level by aggregating and integrating numerous data elements into digital dashboards to analyze the pattern and trends in data.
One of the advantages of modern analytics methods is that users get insight into their business that they need to reach more profitable business plans. Additionally, these tools are being implemented in a range of industries from tech to banking, manufacturing, and government. Data analytics has become a key part of our modern society – from ad agencies to the government.
But what exactly do companies do when they’ve managed to accumulate this data. And more importantly, how much data do they need to accumulate?
How do analytics tools work?
Data analytics tools are designed to help people make better decisions with the right information at their fingertips. Predictive analytics helps identify patterns in data and allows us to use it to guide people, or predict events or behaviors based on past history and prior business activities. We may have seen some of these concepts already: modeling as a method to gain information, optimization as a formula to find patterns and guidance, statistical modeling to demonstrate historical patterns, and predictive modeling to predict the future.
Generally speaking, developing these tools from scratch require specialized skills, in particular data scientists with a wide range of technologies. These individuals typically understand and can execute across multiple disciplines, including data analytics, business modeling, data visualization, advanced analytics, statistics, computer science, engineering and statistics.
For SMBs with limited resources, hiring a dedicated analytics team might be considered a luxury. However, many options are still available for a more cost-effective solution. Partnering with external data consultants, experimenting with self-serve data reporting tools, and even using products powered by advanced analytics are all scalable options available for any budget. One word of caution here – it’s important to ensure that you don’t draw the wrong conclusions from any ad-hoc analysis, especially when using self-serve tools.
Because the relationship between data and the enterprise is more immediate and transparent, a data scientist can add answers to problems that previously had business leaders spending time and energy endlessly debating. Moreover, if these data scientists have access to data across different departments or functions, they can cross-correlate their findings, leading to real root-cause analysis.
Using data, data scientists can be deployed across every department, as often as needed and at varying times. Big Data analysts understand and can validate operational data, which is exactly why information systems data management and the business result management process are vital to generating consistently accurate decision making.
Big Data is a key data asset for businesses and individuals. Ultimately, our ability to leverage it will be our biggest asset.
In the era of big data, every aspect of business is being impacted by innovative thinking to extract business insights through data. As we work to apply advances in technology, we see our future businesses reliant on data for continuous analysis to solve business challenges.
It takes real motivation to transform and pivot a business from a legacy system to an innovative analytics-driven digital business model.
Of course, few businesses will ever be as big as Google, Apple or Facebook. But every business has its own unique data collection regime and what is inferred from it to provide insights and solutions might be vastly different than other companies.
As more businesses continue to digitize, the expectations are that the relevance of these data will improve at every turn. While being able to accurately measure and predict both the positive and negative impact of any action would help improve all enterprises, there will always be winners and losers with which these analyses will be muddled.
Today’s businesses will engage with Big Data – if only slightly in reaction to what Big Data has done for them. In the early part of this decade, business owners were dumbfounded by the power of Big Data and how it could help us predict, engage with and positively affect our audiences. In recent years, the transformative power of Big Data has come to be understood more and businesses are making it into a consistent part of the planning process. We’ve created context around data and have better information to make more informed decisions, which leads to business outcomes that provide better customer experiences, loyalty and ultimately profitability.
Wrapping it up
Business planning and decision making have evolved over time. Companies that employ advanced analytics have the advantage of prioritizing the most critical information while providing the right course of action.
Most importantly, we will have analytics enable intelligent decision making and exploitation of every possible outcome and impact on the customer experience. Using big data to raise the end-to-end quality of insights will drive the next level of innovation and success.
One can think of data analytics as an iterative process:
- Make better business decisions based on data analysis
- Accelerate decision making with predictive capabilities
- Improve leadership quality by improving decision-making ability.
Whether it is customer relations management, the development of content marketing, business insights, revenue optimization or customer relationship management, all aspects of businesses operate under a constant need to gather, organize and analyze information in order to improve business performance. The continued automation of this process will free up business leaders to focus on developing strong and strategic relationships with their customers.
For smaller organizations, there exists a real opportunity to get ahead of the curve. Generally unencumbered by massive legacy systems, these companies will be able to execute much faster than their larger competitors, and set themselves up for bigger success as they grow.