The recent growth of large data sets, better math, and increasingly cost-effective data storage options made “Big Data” accessible to everyone. Marketing Executives, Technology Leaders and Financial Analysts all over the world can now employ deeper analytical rigor as they evaluate business tactics. But does this mean they will make better business decisions? Not until they involve business intelligence analytics product.
Albert Einstein once said,
We cannot solve our problems with the same thinking we used when we created them.
Companies that help customers shift their thinking will be more effective at solving problems and ultimately selling products. Try to frame it as a “From” and a “To”. This is not about bad to good, just better for the current context. As an example, consider companies selling software and services related to “big data.” The shift is not about “simple to intelligent” or “smaller to bigger.” In the area of data, the “aha” might relate to a shift in thinking about decision-making from intuition to analytics, in data models from spreadsheets to algorithms, or how the data is used from target to empower.
One of Kenya’s most provocative thinkers Professor Ochieng never quoted Mark Bonchek or built a content factory like Croton Ville, the World’s Top Corporate University. Perhaps, gave a road-map from mere thinking, insight, or intuition to empowering and enlarging the target audience is necessary. Decision making based on data analysis delineate right from wrong, precepts upon precepts, a priori versus a posteriori truth, antecedent and precedent regulations.
Business Intelligence is a very broad term that means using the data available to your organization to make factually based business decisions. This can take on a number of forms and methods but generally includes doing things such as developing Key Performance Indicators (KPIs), Trending Analysis, Predictive Modeling, dashboards, etc. There are also a few variations of the Business Intelligence Maturity Model which basically categorizes the extent to which an organization leverages BI to make key decisions.
Business Analytics is the heart of BI that doesn’t include the actual business decision making, but the steps that lead up to the decision. Business Analytics is a specialized branch that requires huge amount of data and expensive tools like SAS, SPSS and some other less complicated tools like XL Miner.
How Data-Driven Companies Perform
Taking into account the development of machine learning and the exponential curve of digitized human knowledge. It is observed that the specialists will be replaced by software and even the governments and mega-corporations algorithms will make the decision of more equal division considering the perfect balance of Neumann and incentives, making the society more egalitarian because it will be better for the long-term. But does greed as one of the main variables of human character to model history allow? You can definitely make use of the studies already done by companies by simply observing and appreciating how they might have used the business analytics tools.
Where’s the evidence that using big data intelligently will improve business performance? What this change in the paradigm of decision means in a time series of approximately 50 years. If this path really proves possible given the countless variables that can change it as world wars and natural catastrophes for example. Would not that mean dehumanizing humanity? Throughout the history of technology we see the effort to separate the package. But to be something you need the whole package. To create identity requires the good side and the bad side. Mankind has to change like the river and man. We just wanted to raise this question that we believe is linked to this paradigm shift.
How Intelligent Narrative Is Revolutionizing Business
Today’s enterprises have Big Data problems–a data collection that has grown so big that it has become difficult to handle it using traditional way. The explosion of data sources, technology, and business intelligence analytics is yielding a wealth of new business opportunities to provide success in today’s business market with self-service analytics. Wealth management companies are using deep learning solutions for long-term value investing. They need innovative knowledge breakthrough, integrating country central banks, monetary, economic, fiscal policy impact on sectors supply, demand, company earning based asset pricing, market timing, long short strategy maximize risk adjusted return all with the intelligent narrative and deeper context of data.
Quantenstein is just one example of such an integrated software platform for automated long-term value investing that builds on the latest developments in Deep Learning technology. For a given investment universe (e.g. regions, industries, market cap categories) and set of constraints (e.g. portfolio size, dividend yield, holding period, transaction costs, ESG criteria), Quantenstein optimizes client-specific financial performance metrics based on large quantities of fundamental accounting data to assemble tailored investment portfolios.
Business Data Analysis
Data centric style of decision making is Analytics. There is some campaign that your organization will be doing. Lot of marketing money is at stack. You review past data of campaigns and suggest where your marketing money needs to be spend. The purpose of any data and any system is to enable your business and be part of your Decision Support System. Intellectual analysis and context combined with data are key to making good decisions.
Real-time ad-hoc data exploration to identify what drives key metrics
The over-riding aim of business data analysis must be to support great Customer Relationship Management. Any business investment that those not ultimately tend towards improving customer satisfaction is missing on the fundamental bedrock of the enterprise. Business data should lead to a better understanding of the belying motivations of the customer. When this is known, strategy formulation can be better on point.
Context is everything with analytics. Without deep context, data is just data. No purpose, no intelligence means no resultant decision benefit. But do not underestimate the complexity and commitment required to build this complex relationship. Also, in cases, when we are talking about data, we more likely are directed to math, science, machine, etc. We tend to forget the final audience of data display are human eyes. Business data is not only about being scientific, but also about bringing out the art to people. This is where innovation is necessary.
Where is the hurdle?
A big obstacle to companies effectively using business data can be their business analytics product’s limitations or inability to harvest data in the first place and a lack of clarity and consistency across an organization about what exactly they want to achieve with that data without the use of differentiation in the intelligent narratives (Dugas, 2017). Remember you are not in a business of technology for employing technology personnel to build those intelligent narratives. Ideal way is to select a BI platform that works on cloud which demands no hardware investments or any special skills to deploy and manage the system.
What kind of intelligent narratives lead to better outcomes in business intelligence for the analytics product?
The answer from constructive developmental psychology is that business intelligence analytics product not only creates an intelligent narrative about the world, these are also very much aware about how they bring deep context about the data. A successful analytics product is always aware that any problem that is ‘simple’ is one that has been simplified, by someone for some purpose. (S)he will for example see that the business model canvas, an agile framework, a flexible value chain optimization model, an IT architecture, or a customer interaction approach will always have unidentified or hidden dimensions and consequently the model does not reflect what is happening in the ‘real world’. A comprehensive BI analytics product uses intelligent narrative and bring context to data, and then does both while doing. So it has mechanisms that enable it to conclude beyond dominant narratives, beyond what is salient.
Differentiation among Competing Business Analytics Technologies
Tableau is presently the “gold standard” of visualizations in this software category, but there are alternatives that might better fit your team – whether you’re a team of one or a department of many, among other considerations. Tableau has a history of being the best for BI visualizations, but others are catching up in light of this past 2016 Gartner Magic Quadrant BI Report where you’ll see there was a big shift and new rules on how they’re ranked. Expect to find many choices with this type of question examples include BIME, Thinklayers, Plotly and Sisense.
Tableau vs Microsoft Power BI
Both solutions provide visualizations. With Power BI you choose the visualization first, then drag the data into it. In Tableau, you select the data and switch between visualizations on the fly. It’s easier to jump between visualizations in Tableau. When it comes to presenting data visually, both solutions take a different approach. Tableau has always had a strong focus on visuals, while Power BI has developed its data manipulation features first, but provides access to simple visualizations as well.
Tableau vs IBM Cognos
When BI products are compared to Tableau, visualizations are always a significant consideration because Tableau is known as the best in this regard. If your sole purpose for a BI solution is to quickly create and share data visually, Tableau is a top contender. If visualizations are only a portion of what you’re looking for, a full BI platform such as Cognos could be a better fit.
Tableau vs Spotfire
Tableau provides superior visualizations for graphs, dashboards and reports, but Spotfire also provides fine visuals compared to most BI tools. One detail that shouldn’t be overlooked with both solutions is that they can each visually display outliers in a dataset. Many BI solutions for visualizations can accurately interpret trends, but not outliers. When a solution can’t spot anomalies that skew trends, users make inaccurate assumptions. Tableau and Spotfire do not have this flaw. Tableau Public is free, embeddable most anywhere, super easy to use, and has support for tons of cool viz types (bullets, sparklines, etc). This is more of a visualization and inductive (vs. technical) analytics tool. Spotfire is not a web service per se. However, visualizations created using the software can be viewed in a browser. Spotfire supports the S+/R libraries from what we understand. There are more technical analytical elements in this tool.