Investment in Business Intelligence (BI) ventures continues to be a main focus for companies, and developing fields like predictive analytics are growing rapidly. Businesses are looking to utilize their ever-expanding data resources, in order to deduce market trends more competently, enhance product superiority, and gain ground on the competition.
Traditional BI solutions use ad hoc query, reporting tools and dashboards to look at current and past company information. Historically, BI only answered questions related to what has already happened, or why this particular event might have occurred. On the other hand, predictive analytics examines large quantities of current and former information, and then builds a model to project what could happen in the future, based on past trends. Even in a cash-strapped economy, with meager IT budgets, companies are spending a portion of their profits on Business Intelligence, and predictive analytics.
Predictive analytics tools are not new concepts, and have been around for many years from conventional sellers, such as SPSS and SAS Institute, and subsequently, other vendors broke into the predictive analytics market. However, widespread adoption of this business tactic was delayed, largely due to the level of specialization and the complex nature of available tools.
Making Money On Predictive Analytics
As a result, finance experts and statisticians have now welcomed predictive analytics tools. These professionals use them as a way to assess possible credit risks, and recognize fraudulent actions. In the retail sector, predictive analysis tools are used to predict opportunities to cross-sell and up-sell within the current customer base. Additionally, telecommunications businesses use these tools to predict customer activity, like payment rates.
Today, sectors typically not associated with predictive analytics, such as manufacturing, are beginning to see the potential of progressive analytical resources. Industry experts are now recommending that businesses invest in predictive analytics tools. If the business has a repeatable process, and produces large quantities of data, then a minor improvement in the implementation of the process could potentially save money, or create added revenue.
For instance, manufacturers could use this technology to determine the mean time between failure (MTBF) rates for any number of processes, helping them to optimize quality efforts. In addition, manufacturers can use these tools to forecast supply chain holdups and aid in managing materials.
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BI vendors are responding to increased customer demand, by offering predictive analytic tools paired with other progressive analytic resources. Certain companies, like SAP, have joined with established industry leaders, while others, such as IBM, have made crucial acquisitions (IBM acquired SPSS in July 2009).
Companies who have adopted BI solutions are still developing their applications and expanding their deployment, and some have more work to do before their BI technology is fully integrated. However, BI tools have been made more accessible and are now better suited to everyday business users.
Once predictive analytics grows past its primary audience, then there will be additional challenges, like preparing staff. Whoever is responsible for advanced analytics for a company now needs to have a thorough understanding of the processes and data involved, as well as an experimental mindset. The majority of businesses will not be required to hire professional statisticians. Instead, they can use specially trained business analysts, or BI experts.
Data Analytics And Business Intelligence
These dedicated teams will spend the majority of their time preparing and cleaning data. Data analytics, scoring models, regression models, and data mining logic all need to be moved to the enterprise data warehouse. This way they can be accessed throughout the entire organization.
Should you want more information on how BI and predictive analytics can help you achieve better business outcomes, please contact us today for more details.In the last two years alone, 90% of the world’s data has been created (Source: IORG, 2019). Business leaders understand that their data is valuable, but many businesses collect data and static business intelligence reports but struggle to find a path from business intelligence reporting to analytics.
The purpose of Business Intelligence is to optimize the business, not simply create reports. When allocating budgets, it can be hard to prove a return on investment in Business Intelligence projects since the output is a series of reports or dashboards rather than a demonstration of business optimization.
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Further, in the competition for budget, Business Intelligence projects can lose out to technology upgrades where it is easier to demonstrate business optimization and value. Organizations often need to prove a return on investment, and it is hard to provide evidence for return on investment where the organization limits Business Intelligence activity to patterns of simple, static reporting. This constraint means it is harder to embark on a path of optimization through Business Intelligence and then onto further optimization through analytics.
How can organizations break out of this pattern of simply storing volumes of raw data to gaining valuable insights? Gartner put forward a suggested roadmap to categorize the purpose of reporting and analytics as organizations proceed through their journey from static business reporting to optimization through Business Intelligence and analytics.
. This stage is characterized by traditional static Business Intelligence reports and visualizations such as pie charts, bar charts, or line graphs. At this point, Business Intelligence has an operational focus to gather data, generate reports, and produce ad-hoc reporting. Often, this stage involves basic reports, and it may include flagging under-performing areas that require attention.
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Organizations usually stop at this point, believing that this is what ‘good’ looks like for a successful data-driven organization. However, this is a good starting point rather than an end. The objective is to move the organization from the ‘Descriptive Analytics’ stage towards optimizing the organization.
Business analytics has the objective to predict and prescribe actions with the overall goals in mind. Analytics needs good Business Intelligence as a foundation. Without good Descriptive Analytics, it will not be easy to make predictions or find good connections between different siloed datasets.
Stage in the Gartner process. The Predictive Analytics stage emphasizes prediction rather than description. In Gartner’s approach, analytics are intended to be easily consumed by accessible tools and it promotes rapid, relevant analytics. For some organizations, current architecture will not support prescriptive or predictive analytics because there is no opportunity to collaborate with data scientists.
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Business Intelligence is often categorized as an IT function that uses data and technology rather than a business function aimed at business performance optimization. Moving from simple Business Intelligence to analytics can involve moving from an IT mindset about data to a business mindset focused on outcomes. From the IT perspective, this shift means more than reducing storage and managing data costs. Instead, the focus shifts to analyzing the data and using it to help drive decisions and further analytical questions.
Stage is an excellent place to start since the initial data must be correct, and more complex analytics will not work correctly unless the descriptive stage is in place.
From the Gartner definition, it is possible to dive deeper into how organizations can move through the maturity stages from descriptive Business Intelligence, which Gartner describes as
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How can we move from business intelligence technology to technology that supports predictive analytics? When we look at Azure, for example, the range of Azure technologies plus the sheer cadence of Azure updates means that it is easy to get lost in all options.
When you are architecting a system, how do you know what datastore option to choose? And what’s the tipping point to move you from one solution to another? Is it data size, data complexity, cost? The list goes on, which means that we introduce complexity. Let’s start with looking at storing data through to creating a data platform to support predictive analytics.
In today’s multi-cloud environments, organisations have data in different clouds as well as on file servers, and possibly even on their laptops. As a first step, the organisation should have an inventory of the data sources that they use on a regular basis. As the organisation proceeds to understand their data better, one common question arises of where the data should be stored; in a data warehouse or a data lake?
Business Intelligence Verses Predictive Analytics
In Business Intelligence, data warehouses continue to be popular because they are very mature technologies. They work well with tools such as Excel, Power BI and Tableau. Data warehouses perform well because the data is wholly curated and structured to answer specific query patterns quickly.
Organisations can use data lakes to improve their analytical processes. Here, a data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed.
Data lakes address the shortcomings of data warehouses in two ways. First, in data lakes, the data is stored in structured, semi-structured, or unstructured formats. Second, the data read process determines the data schema rather than writing the data. Data Lakes are popular because of their cost-effectiveness. There is never any need to throw away or archive the raw data, and it is always there should any of the analysts want to revisit it.
Business Analytics / Intelligence
Azure Data Lake stores data for advanced analytics and large volumes of unstructured data. At this point, this is the semi-structured or unstructured data sitting in data lakes. Processes are run on this data to yield Business Intelligence answers and analytics insights.
If the organization
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