5 Ways Big Data Is Helping Businesses Overcome Obstacles & Improve KPIs
Knowledge is power and access to and use of big data is a literal example. The depth and veracity of big data make it an incredible business resource. So much so that it has been dubbed the “new oil” of the digital economy.
With big data tools, businesses are navigating the increasingly large and dense global digital landscape. More than ever, companies are making data-driven decisions in terms of staffing, production, marketing, strategy, and so on. In every sector, key performance indicators (KPIs) are measured more efficiently than before big data came into the scene.
This article explores how using big data analytics helps businesses overcome obstacles and improves overall business performance.
What Is Big Data?
Big data are data sets that consist of greatly varied elements arriving at high velocity and expanding continuously. These types of data sets are much too complex to be processed with traditional data processing tools. As such, big data is usually processed with Artificial Intelligence systems and Machine Learning software.
Big data software can be expensive to set up and maintain compared to traditional data processing tools.
Twitter feeds, for example, make up a set of big data, and so are emails and website clickstreams. Big data can amount to dozens of terabytes or hundreds of petabytes. Big data tools make it possible to collect these data swiftly and react to the insights almost immediately. Facebook’s over 500 petabytes per day of data collection and processing power is an excellent example.
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Get a complete view of every customer Why Use Big Data?
Big data software can be expensive to set up and maintain compared to traditional data processing tools. But for very good reasons both big companies and small businesses are doing the most to get the big data experience. That’s because businesses using big data are solving problems in real-time, making informed decisions, and performing better overall than they did without it.
Compared to traditional data processors, big data processing tools work at a higher capacity. They provide business executives with actionable and far-reaching insights. With these, executives are able to make quick data-driven decisions that enable businesses to seize time-sensitive opportunities faster.
Big data tools may be expensive but they actually are cost-effective for some reasons. Storing large amounts of data is easier and more efficient with big data software. Also, the stored data is used to provide businesses with cost-cutting insights in other areas of their enterprise.
Issues like high employee turnover can be addressed using big data as both predictive and corrective measures. When firms tie their diversity, equity and inclusion goals to measurable KPIs, they can use big data to measure their progress on a larger and deeper scale.
If big data tools did not exist, it would take thousands of employees and thousands of spreadsheets flying around, not to mention hundreds of thousands of billable hours, to manually analyze these data and make better-informed decisions.
Top Big Data Tools for Your Business
Depending on your business needs, goals, and organizational size, several big data tools can give you the advantage that you need in today’s increasingly competitive business landscape. After reviewing business needs, from small startups to established corporations, these are our top recommended big data tools to help you store and process your ever-growing data.
- Microsoft Power BI
- SPSS
- Tableau
- MongoDB
5 Ways Companies Are Using Big Data to Improve KPIs and Overcome Obstacles?
There’s no doubt that leading brands are using big data to win. But how are they doing that?
Here are five ways that these top companies are leading with big data.
1. Better customer experience
Good customer service is key to business success, businesses lose about $1.6 trillion from customers lost to a competitor. Companies can’t speak individually to their thousands or millions of customers scattered all around the globe. It would be far too expensive and exhausting to build a customer experience database like that, but big data bridges that gap. Companies can look at the digital footprint of consumers and get key information. With this information, they deliver personalized experiences to their consumers.
Customer data platforms (CDPs), customer relations management platforms (CRMs), and customer analytic tools are data customer analytics tools used for this particular purpose. They bring users closer to service providers and manufacturers. Through customer experience data, companies can measure customer satisfaction and figure out why some products are preferred over others. But how are big brands really using these tools?
Disney’s Magic Band feeds its data systems with guests’ biodata, behavior, and preferences. With these, they are able to offer personalized experiences like having Disney characters greeting your kids by name. Netflix, Amazon, and LinkedIn also use big data to offer personalized product recommendations to users.
2. Deeper, wider, real-time performance measurements
Big data has changed what things we can measure and how we measure them. Today, companies using big data have more information and therefore more choices of KPIs. They can expand the performance scope of their business and measure results from multiple indication points.
One big improvement experience that big data has brought to KPIs is the accuracy of data analysis. Traditionally, surveys, interviews, and spreadsheets are the proxies used to glean KPI data. Now, for some personal reasons, people may not be completely honest in surveys or interviews. In fact, over 25% of internet survey participants don’t even read the survey questions before responding. As a better alternative, companies use direct data from sensors, social listening, and the online behavior of users and employees to create and measure KPIs more accurately.
Another big data importance to KPIs is how it eliminates lags. Big data tools don’t just measure past events, they track performance in real-time and make appropriate adjustments without strict supervision.
3. Targeted advertising and marketing
Advertising and marketing can be very costly ventures. And many times, businesses have lost millions in fancy ads that were inefficient. Apart from targeting the wrong audience, businesses also contend with losses due to ad fraud. Big data is helping businesses market more efficiently.
By collecting data from web visits, search engine queries, point of sale transactions, etc, marketers can detect nuances in consumer behaviors. They use sophisticated analysis to achieve focused and targeted campaigns. Big data, in advertising, also helps analyze factors like demography, geography, and spending habits. Google ads, for example, use complex algorithms to create a target audience for digital campaigns. The algorithms consider these factors (spending habits, web activities, search engine queries, etc) to deliver precisely targeted ads.
A specific type of big data analysis tool, customer analytic tools, also helps businesses identify and pursue promising sales leads. With it, marketing teams can leverage big data insights to build and maintain brand image strategically.
Furthermore, big data analytics helps leading brands to identify patterns that reveal hidden activities of clever ad fraudsters. So, they prevent huge losses in consumer data, time, and hundreds of millions of dollars in privacy violation fines.
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4. Big data analytics used for risk management and problem-solving
If a business is to remain profitable, the ability to spot potential risks and set up contingencies is a must-have. And it takes more than just having business insurance to mitigate business risks. Risk managers need to prepare for the possibilities of events like a pandemic, natural disasters, and economic meltdowns, among several other unpleasant occurrences. Big data tools make it possible to analyze data across multiple entry points and identify and quantify possible business risks; both avoidable and inevitable.
Regarding problem-solving and bug fixing, companies with heavy machinery can set up predictive maintenance using unstructured data from consumers’ log entries, error messages, and sensor data. They can cross-reference the data with the make, model, production year of the equipment as well as weather data to mitigate mechanical failures.
Big data reveals not-so-obvious patterns, insights, and correlations that give organizations a competitive edge they didn’t have before. Underlying issues secretly eating away at the firm’s bottom line are exposed; thanks to the depth and versatility of big data. With hidden problems now revealed, companies can develop solutions. Ironically, before the adoption of big data analytics, companies may have accepted that these problems were insurmountable.
5. Product development
Innovation in product development is largely guided by big data insights. Research has always been an important aspect of product development and it has gotten more critical with big data. Data analytics from social media, focus groups and test markets help businesses identify key features of current and past products. From these insights, companies can decide which feature to scrap or redesign.
Breaking into a new market entirely is something big data helps companies decide and execute. Companies can identify, verify and measure the need for new products before going ahead to design them. Basically, big data is opening the eyes of businesses to additional revenue streams. Amazon Fresh and Whole Foods are good examples of innovation driven by big data.
Conclusion
Big data analytics tools are important tools for business success today. Many companies are planning their marketing, business, and risk strategies based on insights obtained using big data. Data has proved essential in maintaining customer satisfaction and building strong audiences.
The scope of analysis available with big data has also improved KPIs. Firms look for productivity indicators not just in surveys but in real-time events and employee/consumer behaviors.
