Business forecasting involves making informed guesses about certain business metrics, regardless of whether they reflect the specifics of a business, such as sales growth, or predictions for the economy as a whole. Financial and operational decisions are made based on economic conditions and how the future looks, albeit uncertain. Show
Companies use forecasting to help them develop business strategies. Past data is collected and analyzed so that patterns can be found. Today, big data and artificial intelligence has transformed business forecasting methods. There are several different methods by which a business forecast is made. All the methods fall into one of two overarching approaches: qualitative and quantitative. While there might be large variations on a practical level when it comes to business forecasting, on a conceptual level, most forecasts follow the same process:
Once the analysis has been verified, it must be condensed into an appropriate format to easily convey the results to stakeholders or decision-makers. Data visualization and presentation skills are helpful here. There are two key types of models used in business forecasting—qualitative and quantitative models. Qualitative models have typically been successful with short-term predictions, where the scope of the forecast was limited. Qualitative forecasts can be thought of as expert-driven, in that they depend on market mavens or the market as a whole to weigh in with an informed consensus. Qualitative models can be useful in predicting the short-term success of companies, products, and services, but they have limitations due to their reliance on opinion over measurable data. Qualitative models include:
Quantitative models discount the expert factor and try to remove the human element from the analysis. These approaches are concerned solely with data and avoid the fickleness of the people underlying the numbers. These approaches also try to predict where variables such as sales, gross domestic product, housing prices, and so on, will be in the long term, measured in months or years. Quantitative models include:
Forecasting can be dangerous. Forecasts become a focus for companies and governments mentally limiting their range of actions by presenting the short to long-term future as pre-determined. Moreover, forecasts can easily break down due to random elements that cannot be incorporated into a model, or they can be just plain wrong from the start. But business forecasting is vital for businesses because it allows them to plan production, financing, and other strategies. However, there are three problems with relying on forecasts:
Negatives aside, business forecasting is here to stay. Appropriately used, forecasting allows businesses to plan ahead for their needs, raising their chances of staying competitive in the markets. That's one function of business forecasting that all investors can appreciate.
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions. On a broad scale, data analytics technologies and techniques give organizations a way to analyze data sets and gather new information. Business intelligence (BI) queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by analytics systems. Why is big data analytics important?Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals. How does big data analytics work?Data analysts, data scientists, predictive modelers, statisticians and other analytics professionals collect, process, clean and analyze growing volumes of structured transaction data as well as other forms of data not used by conventional BI and analytics programs. Here is an overview of the four steps of the big data analytics process:
Key big data analytics technologies and toolsMany different types of tools and technologies are used to support big data analytics processes. Common technologies and tools used to enable big data analytics processes include:
Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming analytics applications are becoming common in big data environments as users look to perform real-time analytics on data fed into Hadoop systems through stream processing engines, such as Spark, Flink and Storm. Early big data systems were mostly deployed on premises, particularly in large organizations that collected, organized and analyzed massive amounts of data. But cloud platform vendors, such as Amazon Web Services (AWS), Google and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud. The same goes for Hadoop suppliers such as Cloudera, which supports the distribution of the big data framework on the AWS, Google and Microsoft Azure clouds. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses. Big data has become increasingly beneficial in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources. Big data analytics is a form of advanced analytics, which has marked differences compared to traditional BI.Big data analytics uses and examplesHere are some examples of how big data analytics can be used to help organizations:
Big data analytics benefitsThe benefits of using big data analytics include:
Big data analytics challengesDespite the wide-reaching benefits that come with using big data analytics, its use also comes with challenges:
History and growth of big data analyticsThe term big data was first used to refer to increasing data volumes in the mid-1990s. In 2001, Doug Laney, then an analyst at consultancy Meta Group Inc., expanded the definition of big data. This expansion described the increasing:
Those three factors became known as the 3Vs of big data. Gartner popularized this concept after acquiring Meta Group and hiring Laney in 2005. Another significant development in the history of big data was the launch of the Hadoop distributed processing framework. Hadoop was launched as an Apache open source project in 2006. This planted the seeds for a clustered platform built on top of commodity hardware and that could run big data applications. The Hadoop framework of software tools is widely used for managing big data. By 2011, big data analytics began to take a firm hold in organizations and the public eye, along with Hadoop and various related big data technologies. Initially, as the Hadoop ecosystem took shape and started to mature, big data applications were primarily used by large internet and e-commerce companies such as Yahoo, Google and Facebook, as well as analytics and marketing services providers. More recently, a broader variety of users have embraced big data analytics as a key technology driving digital transformation. Users include retailers, financial services firms, insurers, healthcare organizations, manufacturers, energy companies and other enterprises. |