Tag Archives: Business intelligence

10 Essential Skills for Business Analysts in 2024

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10 Essential Skills for Business Analysts in 2024

10 Essential Skills for Business Analysts in 2024

In modern business terrain, business analysts play a crucial role in driving business success. Companies strive for data-driven insights, and streamlined processes and only skillful business analysts can cater to these needs. In this article, we will discuss about 10 essential skills that are essential for business analysts to thrive in today’s complex business environment.

Business Intelligence Tools:

BI tools, such as SAP BusinessObjects, IBM Cognos, and Microsoft Power BI serve as a bridge between raw data and actionable insights. SAP BusinessObjects, IBM Cognos, and Microsoft Power BI are all prominent BI platforms that facilitate the transformation of complex datasets into easily digestible and visually engaging formats. Business analysts leverage these tools to present key performance indicators, trends, and patterns in a manner that is accessible to stakeholders.

Data Analysis:

Proficiency in data analysis tools and techniques is essential for business analysts to extract valuable insights from large datasets. Expertise in SQL allows analysts to interact with databases, executing queries to retrieve and manipulate data. Microsoft Excel serves as a versatile tool for data organization and preliminary analysis. It also provides a familiar interface for tasks such as data cleaning and basic computations. Furthermore, the utilization of data visualization tools like Tableau and Power BI enhances the communicative aspect of data analysis.

Requirement Management Software:

Requirements management tools play a crucial role in the systematic handling of project requirements from inception to completion. JIRA is a widely used tool for project management and issue tracking. Confluence, on the other hand, is a collaborative platform that allows teams to create, share, and collaborate on documents. IBM Rational DOORS offers capabilities for capturing, tracking, and analyzing requirements in a structured manner. Proficiency in these tools enables business analysts to document project requirements, which ensures that all stakeholders have a clear understanding of what needs to be achieved.

SQL:

Database knowledge and SQL proficiency is crucial when dealing with structured data. Business analysts often engage with large volumes of structured data, and a solid understanding of both relational databases, such as Microsoft SQL Server, MySQL, and Oracle DB, as well as NoSQL databases, is deemed essential. Relational databases are fundamental to data storage and retrieval in many organizations, and business analysts are expected to be adept at working with them. The ability to write and execute SQL commands is crucial, as it enables analysts to access, retrieve, manipulate, and analyze data effectively.

Process Modeling:

Business analysts are expected to possess knowledge of process modeling techniques, especially BPMN (Business Process Model and Notation). Additionally, proficiency in process analysis tools such as ARIS or Visio is crucial for effectively mapping, analyzing, and improving business processes.

 

Read more at 10 Business Analyst Skills Ensuring Business Success in 2024

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Blue Print for Success

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Falling demand, tight credit, and unprecedented economic challenges are forcing businesses to look for tools and means of reducing cost, increasing productivity, maintain-even increase customer base, avoiding costly initiatives, and improve quality. The essence of this initiative, Supply Chain Institute, is to address these problems by providing analytics, modeling capability, and execution methods to assist with the resolving of the real life business problems.

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Announcement

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Greetings All,

In response to industry needs in utilization of captured data and through visibility functions, we will start webinars on creating value by using capturing of selected array of data as BI. This is combined with risk and performance analysis to accommodate for optimality in Operational and Strategic optimality.

 

Stay tuned,

Dr. Javad Seyed

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What’s the Difference Between Business Intelligence (BI) and EPM?

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Business Intelligence Emerges From Decision Support

Although there were some earlier usages, business intelligence (BI) as it’s understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s. Then in 1989, Howard Dresner (a former Gartner analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” In fact, Mr. Dresner is often referred to as the “father of BI.” (I’m still trying to identify and locate the “mother of BI” to get the full story.)

The more modern definition provided by Wikipedia describes BI as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes.” To put it more plainly, BI is mainly a set of tools or a platform focused on information delivery and typically driven by the information technology (IT) department. The term “business intelligence” is still used today, although it’s often paired with the term “business analytics,” which I’ll talk about in a minute.

Along Came Enterprise Performance Management

In the early 1990s, the term “business performance management” started to emerge and was strongly associated with the balanced scorecard methodology. The IT industry more readily embraced the concept around 2003, and this eventually morphed into the term “enterprise performance management” (EPM), which according to Gartner “is the process of monitoring performance across the enterprise with the goal of improving business performance.” The term is often used synonymously with corporate performance management (CPM), business performance management (BPM), and financial performance management (FPM).

Read more at What’s the Difference Between Business Intelligence (BI) and EPM?

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Supply Chain Management in QlikView / Qlik Sense

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With complicated structure in a supply chain, it has been a challenge for executives to see and understand the associated changes and movements in a supply chain. Examples are raw materials, inventory, products, marketing campaigns, promotions, and other supply chain activities.

As brands and products proliferate, are spun off, and re-consolidated, supply chain companies find themselves struggling to understand what they have, what they need, and where they’re going. Doing so requires a tremendous amount of data, drawn from both external sources (suppliers, partners, customers) and internal ones (marketers, production managers, supply chain groups). The ability to see all of the data surrounding a brand at a glance is a tall order, one only made harder by the proliferation of systems and processes designed to support it. Before companies can profit from efficiencies of scale, they need to consolidate these systems. This is an area where business intelligence (BI) can help them.

However, melding disparate data sources through business intelligence turns out to be a disaster, when companies are using multiple technologies under one roof. These technologies could be from Microsoft, Oracle, SAP, IBM, Teradata, and others. People are struggling before BI implementation, and people are struggling even more after it. As a result, instead of placing information in the hands of the managers who needed it, they are now locked inside those data and technologies, where they could barely get to the real BI they desperately need. On the other hand, IT departments are struggling with questions like: “how many people I needed to build reports”, “how long it is going to build reports”, and “what those reports should look like”.

To meet the challenges of data and technologies, a possible solution is QlikView or Qlik Sense from Qlik. Compared to other BI vendors, the most unique feature of QlikView / Qlik Sense is that people don’t have to think about the joins of tables; people don’t even have to think about which tables to pull out of their ERP. The appliance just bolts onto the side and sucks the whole thing out. People, or even non-IT people, can spent a week extracting the relevant data tables from the central data warehouse, then loading them into QlikView / Qlik Sense as individual data sets — one for sales, one for materials management, and so forth. And, suddenly, they can gaze across a total landscape of its supply chain before drilling down by product or brand or segment or market — or any combination it liked.

With QlikView / Qlik Sense, companies can train or hire a handful of savvy managers who in time became the trainers for their respective divisions. When the need arises for a report, they’ll point you to an existing report or enhance it or build a new one if need be, if everyone agrees it’s the right thing to do. People are taking reports into their own hands and customizing them to suit their needs.

In addition to this special feature, people can also implement their supply chain management BI by using one of the following templates in Qlik Demo site:

  • Executive Insights
  • Production Insights
  • Forecasting and Planning
  • Sourcing and Supplier
  • Regulatory Compliance
  • IT Management
  • Warehousing and Distribution
  • Transportation and Logistics
  • Merchandise Management

In addition, people can find other supply chain solutions provided by Qlik vendors at the Qlik Market. A screenshot of the Order and Inventory Management Dashboard is enclosed below. You can go to its interactive demo site here.

Order and Inventory Management.qvw

In summary, supply chain management is implemented in QlikView / Qlik Sense as applications or reports in all areas of the supply chain management, which can come from one of a reports template Qlik provides, custom made to match what you have today, or created by one of its vendors. The applications and reports do not need specialized IT departments to create and can be created by your very own people in the field.

References:

 

How Do You Turn Supply Chain Data into Actionable Information?

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How Do You Turn Supply Chain Data into Actionable Information?

There is a continuum in terms of presentation of data that allows for continuous sophistication in understanding and interpreting data. There are lots of ways to view data, but three that are particularly useful in supply-chain analytics are –Reporting, Scorecarding, and Benchmarking.

The simplest form of looking at data is what we have all seen dozens of times, we call it “Reporting”. Back in the day, reporting was numbers printed out on green bar paper, but today’s business intelligence reports are far more detailed and dynamic than in the past. For instance, a BI report of today displays all the data about transportation providers as usable information, in a scorecard format. Factors such as on-time delivery, freight cost per unit shipped, and transit time are assigned metrics and weighted averages to help users determine how well carriers are performing overall.

Operation managers and executives who want a quick, daily overview of what is happening in their transportation or supply chain network use dashboards to provide information in near real-time to help users understand what is happening within their network, and allows them to make proactive decisions to remedy problems as they occur. Where reporting is really like looking in the rearview mirror, dashboards are used to see what’s going on now, and makes it easier for users to identify trends and exceptions, and to intervene before something goes wrong.

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Hadoop and Data Warehouses

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Hadoop and Data Warehouses

I see a lot of confusion when it comes to Hadoop and its role in a data warehouse solution.  Hadoop should not be a replacement for a data warehouse, but rather should augment/complement a data warehouse.  Hadoop and a data warehouse will often work together in a single information supply chain: Hadoop excels in handling raw, unstructured and complex data with vast programming flexibility; Data warehouses, on the other hand, manage structured data, integrating subject areas and providing interactive performance through BI tools.

There are three main use cases for Hadoop with a data warehouse, with the above picture an example of use case 3:

1. Archiving data warehouse data to Hadoop (move)
Hadoop as cold storage/Long Term Raw Data Archiving:
– So don’t need to buy bigger PDW or SAN or tape

2. Exporting relational data to Hadoop (copy)
Hadoop as backup/DR, analysis, cloud use:
– Export conformed dimensions to compare incoming raw data with what is already in PDW
– Can use dimensions against older fact table
– Sending validated relational data to Hadoop
– Hadoop data to WASB and have that used by other tools/products (i.e. Cloud ML Studio)
– Incremental Hadoop load / report

3. Importing Hadoop data into data warehouse (copy)
Hadoop as staging area:
– Great for real-time data, social networks, sensor data, log data, automated data, RFID data (ambient data)
– Where you can capture the data and only pass the relevant data to PDW
– Can do processing of the data as it sits in Hadoop (clean it, aggregate it, transform it)
– Some processing is better done on Hadoop instead of SSIS
– Way to keep staging data
– Long-term raw data archiving on cheap storage that is online all the time (instead of tape) – great if need to keep the data for legal reasons
– Others can do analysis on it and later pull it into data warehouse if find something useful

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The TOFU (Top of Funnel Users) Approach to Business Intelligence

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The TOFU (Top of Funnel Users) Approach to Business Intelligence

An interesting article in Forbes.com entitled, “Why Top Of The Funnel BI Will Drive The Next Wave Of Adoption”, written by Dan Woods, sparked some great conversations about bottom of the funnel users (20-30% wanting specific business information), and Top of Funnel Users (or TOFU) that want to interact with information in a personalized way and express their interests. I was fortunate to have Matt Milella, Director of Product Development for Oracle Business Intelligence Mobile Apps, and Jacques Vigeant, Product Strategy Director for Oracle Business Intelligence & Enterprise Performance Management, join me for a podcast to discuss their opinions about “The TOFU approach to business intelligence (BI)”.

Jacques explained that the article is basically about how BI has historically focused on what we refer to as the ‘business analyst’ or the ‘power user’. That’s the person in a company that has the unenviable task of analyzing data, finding trends, and synthesizing data into dashboards that he/she then shares with management. The common thinking, in BI companies, is that roughly 20% of the users prepare data that the ‘rest of us’ consume. There are many practical and technical reasons why BI started using this model 30 years ago, but the world of technology has come a long way since then. Today, the average user can do much more with much less help from IT.

Do you think that this article is interesting? Do you have any opinions? Thank you for reading. If you have any questions, send us a messageor leave a comment below.

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FusionOps Unveils LiveAnalytics for Supply Chain Data

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FusionOps Unveils LiveAnalytics for Supply Chain Data

FusionOps, a supply chain analytics company that provides a cloud-based business intelligence (BI) application, has launched LiveAnalytics for supply chain data. LiveAnalytics uses images and live metrics to create infographics for supply chain processes and workflows.

The FusionOps application allows businesses to create new analytics from scratch. In addition, the application offers thousands of configurable analytics, metrics and tickers, FusionOps said.

LiveAnalytics leverages FusionOps’ interactive, root-cause analysis across the supply chain. FusionOps said LiveAnalytics users can visualize changes in their supply chains in real-time and evaluate data from all functional areas to become more efficient.

Some of LiveAnalytics’ features include:

  1. Alerts – When alerts are triggered, users are notified via email about supply chain events in real-time.
  2. Key performance indicator (KPI) dictionary – The new KPI dictionary explains pre-built and company-specific metrics.
  3. Personalized navigation – Users can access thousands of dashboards, KPIs and reports directly from LiveAnalytics’ main navigation and “Favorites” menus.

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New Approaches to Analytics to Revolutionize Logistics

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New Approaches to Analytics to Revolutionize Logistics

Three stages are commonly used to categorize an organizations maturity in their use of business intelligence and analytics technologies:

  1. Descriptive: What happened in the past?
  2. Predictive: What will (probably) happen in the future?
  3. Prescriptive: What should we do to change the future?

Descriptive analytics typically means good old fashioned business intelligence (BI) – reports and dashboards.  But, there is a newish technology in the Descriptive category – one that I might argue is worthy of a category in its own right.  That technology is visual data discovery.  The visual data discovery approach has a rapidly growing fan base for many reasons, but one stands out:  It increases the probability that business managers will find the information they need in time to influence their decisions.

Visual data discovery tools typically provide:

  1. Unrestricted navigation through, and exploration of, data.
  2. Rich data visualization so that information can be comprehended rapidly.
  3. The ability to introduce new data sources into an analysis to expand it further.

By helping to answer a different class of question – the unanticipated one – visual data discovery tools increase the probability that managers will find the information they need in time to influence their decisions.  And that, after all, is the only valid reason for investing in business intelligence solutions.

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