Frequently Asked Questions
- Insights
- Frequently Asked Questions
General Questions
Digital transformation is a relatively new term. It refers to organizational change efforts that aim to:
- Improve an organization’s technology and tools
- Enhance employees’ digital skills
- Adopt new, modern business models
Today, the entire economy is digital. Businesses are regularly engaged in digital adoption, transformation, and change efforts .Because of this, the entire global economy is digitizing. Digital transformation helps businesses stay competitive in this environment. those that can transform effectively will survive, thrive, and succeed. Companies that are too slow to adapt
will lose their edge in the marketplace.
Digital transformation is affecting every industry, from retail to marketing to healthcare. The impact of digital transformation differs from industry to industry. The scale of that impact depends on factors such as:
- How reliant an industry is on technology
- What type of technology that industry uses – i.e., hardware or software
- The advancements of technology in general, and for that industry specifically
Regardless of the scale of that impact, every company is transforming to some degree.
A digital transformation project involves a number of steps, such as:
- The digital adoption of new technology, tools, software, and platforms, to maintain or gain a competitive advantage
- IT modernization, from infrastructure to hardware to software
- Implementation of digital-first business strategies
- Refocusing efforts on the customer experience
These disparate efforts follow global trends that prioritize customers and technology. Such trends contrast to “old-fashioned” business models that follow waterfall design approach.
Data mining is the process of digging through large amounts of data which allows businesses to discover patterns and predict future trends. Also known as ‘knowledge discovery in databases’,
data mining is normally applied under three disciplines:
- Statistics
- Artificial intelligence
- Machine learning algorithms
Advances in computing speeds has allowed businesses to automate this process so that they can step away from manual and time-consuming practices. Businesses such as banks, retailers, insurers and manufactures all use data mining to discover patterns in everything from pricing, economic forecasting, competition and social media, so that they can discover how they are affecting their business models, operations and client relationships.
Business intelligence is a term that covers the analytics, tools and processes which are used to optimize performance and make informed business decisions. Business intelligence is an umbrella term which covers:
- Data visualization
- Reporting
- Data mining
- Performance metrics
- Data preparation
- Statistical analysis
- Descriptive analytics
Business intelligence helps companies take an understanding of mass data so that it can be input into an intelligent enterprise model. This strategic approach allows teams and business leaders to identify useful information which would otherwise be lost in mass data.
Data integration is the process whereby you take data from different sources and combine it into one single source of truth. Data integration will often take place during the exploratory data analysis phase, where a researcher will include steps such cleansing, ETL mapping, combining data and transformation.
Data integration will often involve a few elements including a network of data sources, a master server and client data. During this process, the researcher will access the master server for data, extract that data and then consolidate it into a single cohesive data set.
- Charts
- Graphs
- Maps
- Data visualization tools
Data related questions
Data analytics are the tools that are used to analyse raw data so that businesses can make informed decisions to strategies and performance. There are varying tools and processes used in data analytics, many of which are automated through algorithms. These algorithms can quickly reveal specific trends and metrics in mass data that would otherwise be lost.
The best data analytical tools will provide a range of statistical procedures. This allows teams and business leaders to look back and evaluate previous information and look into the future for scenario planning with predictive modeling.
Data analysis is the process whereby information is cleaned, transformed, and modeled so that it can be used to make informed business decisions. These processes will extract useful information from large amounts of data which would otherwise be lost. Teams and business leaders will often conduct this process when evaluating previous business performance, as well as using it to look forward when scenario planning or strategising.
Data mining is the process of digging through large amounts of data which allows businesses to discover patterns and predict future trends. Also known as ‘knowledge discovery in databases’,
data mining is normally applied under three disciplines:
- Statistics
- Artificial intelligence
- Machine learning algorithms
Advances in computing speeds has allowed businesses to automate this process so that they can step away from manual and time-consuming practices. Businesses such as banks, retailers, insurers and manufactures all use data mining to discover patterns in everything from pricing, economic forecasting, competition and social media, so that they can discover how they are affecting their business models, operations and client relationships.
Business intelligence is a term that covers the analytics, tools and processes which are used to optimize performance and make informed business decisions. Business intelligence is an umbrella term which covers:
- Data visualization
- Reporting
- Data mining
- Performance metrics
- Data preparation
- Statistical analysis
- Descriptive analytics
Business intelligence helps companies take an understanding of mass data so that it can be input into an intelligent enterprise model. This strategic approach allows teams and business leaders to identify useful information which would otherwise be lost in mass data.
Data integration is the process whereby you take data from different sources and combine it into one single source of truth. Data integration will often take place during the exploratory data analysis phase, where a researcher will include steps such cleansing, ETL mapping, combining data and transformation.
- Charts
- Graphs
- Maps
- Data visualization tools
- Large amounts of unconnected data from different sources
- No existing formal data governance
- Poor data quality
- Too much reliance on spreadsheets
- Time consuming manual and repetitive tasks
- Teams operating in informational silos
- Confusion over the correct numbers
- No ability to look further into the numbers
- Lack of data to offer insights and support decision making
- Help you understand the needs and behaviors of your clients
- Improve customer satisfaction
- Enable your business to exploit new market opportunities
- Allow you to identify the strength and weaknesses of your product or services
- Allows your business to optimise its resources
- Reduce expenditures
- Increase logistical and operational effectiveness
- Improve revenue and profitability
- Improve financial forecasting and other KPIs
- Allow your business to be more proactive, stay agile and reduce risk by acting under reliable information
- Allow your business to automate time consuming processes and improve efficiency
Business related questions
Until a few years ago, accessing, modeling, visualizing, and reporting the information required by businesses would have been out of the reach of all but the large corporates.
Today, cloud technologies have driven down the costs of implementing BI solutions making them much more accessible for small to medium sized businesses. Specifically, Microsoft’s powerful self-service BI tool, Power BI, allows every business to explore enterprise-grade analytics and decision support for zero upfront cost.
Of course, implementation will vary based on deployment choices, audience mix and visualization requirements. While some vendors provide transparent pricing, others don’t, so you’ll need to take that into consideration. Before you research vendors, it is key to know what your specific budget and requirements are for BI software.
Consider this: On average, the ROI for organisations who leverage Dynamics 365, for every dollar spent is $16.97 in returns.*
Data security and availability are key requirements for any IT system. A BI solution should match the same high levels of performance, reliability, and security that you expect of the other systems in your organisation. Reputable BI solutions leverage existing security infrastructures to keep data safe.
As an example, the Power BI service is built on Azure , Microsoft’s cloud computing infrastructure and platform. Power BI uses Azure Active Directory (AAD) to manage user identity and permissions, and supports Row-Level Security to automatically restrict data access for given users based on role.
While you may have multiple line-of-business systems that you use to manage your business, BI is about combining data from these sources in a structured manner to visually present information in a meaningful way. This is not possible from conventional standalone reporting tools.
A truly useful BI solution should easily connect to your day to day business applications.
Having the right information to make the right decisions at the right time is a simple concept, but making it happen can be a challenge. There’s a substantial amount of data to be mined in every business situation, whether directly from an application, from spreadsheets or even document-based.
A BI solution can aggregate data from all of these sources and more, and provide insights and meaningful information in a single dashboard. Think of a BI Solution as complementary to your existing reporting investments
BI solutions drive high-level discussions to make meaningful strategic decisions. These solutions can access all of your data, both strategic (revenue, profit and growth), and operational (daily sales performance). This enables business leaders to analyze information to make decisions that support business objectives.
An ERP however, is a system used to generate an exact operational view of your organization, often without trend analysis, data comparison, or insights generation. While BI solutions provide strategic analytics, an ERP solution provides transactional, operational analytics.
A good BI solution should support your organisation through a structured process. From an implementation perspective, Business Intelligence is a set of processes, architectures and technologies used to convert raw data into meaningful information to drive profitable business actions. This consists of the following phases:
1. Design
2. Shaping
3. Modelling
4. Visualization
It should be noted that the implementation of Business Intelligence solutions is non-linear. Successful implementations use a circular approach – starting simple, challenging assumptions, refining outcomes, rinse and repeat…
The end goal, however, is a series of reports and dashboards that deliver meaningful data in a way that is accessible and attractive
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