business intelligence

What should business analytics learn from music legends?

Blurred Couple with bicicle stay on the street at sunny day.

In music many legends claim that ‘less is more’. However, all musicians do not share this view, as it is common, say, in more heavy oriented music circles to argue ‘more is more’. This is an old debate between musicians and it now seems that the same discussion should be taken also to modern boardrooms.

view pointLarge databases withhold a lot of strategic information. It is also clear that ever-increasing computational capabilities and storage capacities allow a wide range of new analytics. As new buzzwords like ‘internet-of-things’ and ‘big-data’ are coming true they come with the fact that the number of data sources continue to increase with mind-blowing speed. From measurement perspective this is great, but from analytics perspective it creates challenges.  The immediate outcome is a sharp increase on different metrics that can be measured, which will lead to immense databases of historical row based measurement data.

Modern data based analytics is driven by size and speed. The spotlight in this discussion has been on the rapid development of computational technology and, hence, those developing new technologies control the content of discussion. By focusing on ‘more is more’ the most certain thing is continuous investment to larger and faster IT infrastructure and true business benefits are too often left to sidelines. This discussion is dominated by the question ‘How’How to build infrastructure for analytics?

The critical question in strategic information management should, however, be ‘Why’Why does the firm perform this way? The ultimate goal of analytics should be achieving firm’s strategic goals, which in many cases is firm’s competitiveness that ultimately means maximizing long-term financial performance. We want to shift the focus in analytics discussion to the realized business value.

It is comforting to know that achieving business value with analytics does not mean analysing the whole world but focusing on the critical and substantial parts of firm’s processes. So often we see analysts put to almost impossible situation. Common task is to analyse all data that firm collects, from which the analyst is expected to come up with significant business conclusions. In most cases this is close to impossible at least with the tools in hand!

In terms of data management experts should assess the most valuable measurements for understanding firm’s core business. Scaling down the data requirements calls for deep understanding on the business problem but also thorough understanding of various analytical methods and their capabilities. This task requires a very distinctive but broad skill set. Optimal would be a sort of  ‘strategic analyst’, who possesses deep understanding on both worlds. However, many companies have found the hard way that these specialists are a rare breed. Alternatively, this can be achieved with a special team composed of business professionals and analytics experts.

“Answering these questions will start your firm’s transition from how-analytics towards more impactful why-analytics.”

Customers are often surprised how great of an impact from analysis can be achieved with very limited amount of different measurements. Don’t get me wrong – although we limit the number of different metrics, there is often still a significant amount of row based data from systemic perspective. Our experience has also shown that many companies have missed the value of some simple metrics that need to be measured in order to truly understand their business. Collecting almost incomprehensive amounts of data has lead to false confidence of the completeness of current data collection systems. Simplifying the data stream eases the search of data gaps. In the end, by identifying valuable data and using relatively simple metrics with this focused approach we have been able to increase the business impact of analytics.

We encourage all managers to challenge their current data management with simple questions:

1) Why do we do analytics?

2) What do we want to achieve and why?

3) What are the most essential measurements we need in order to achieve the target?

Answering these questions will start your firm’s transition from how-analytics towards more impactful why-analytics. This is the road towards smarter data analytics that has a real business value.

R.I.P. B.B. King – legend that understood the meaning of less is more

Building competitive advantage with simulation supported logistics

traffic blur motions in modern city hong kong street

In a recent article SCDigest editorial staff analyzes the role of logistic designing as a source of firm’s competitiveness. Still many companies aren’t utilizing advanced methods in designing their logistic chain and only see the topic “a necessary but not a critical routine”. According to supply chain management experts, these companies are missing an opportunity to minimize costs and maximize effectiveness. The key is to view logistics as a process that can be constantly developed and not a separate function in the company.

Our experience at SimAnalytics is very much in line with the SCDigest authors and similar conclusions have also been made in other outlets. A logistic survey in Finland concluded that 35%-43% of the company competitiveness for large organizations originates from logistics. Further conclusion was that 40%-50% of this competitiveness could be affected by company’s own actions and decisions.  Altogether it’s fair to say that logistics is an important process in building firm’s competitive advantage. Furthermore, it is reasonable to argue that there is great potential in advanced methods in logistic development.

The design of logistics is critical especially during the times of change. Decision makers face questions like “what kind of logistic system the company needs when extending its operations” or “how the company should organize manufacturing and delivery of a new product”. These types of questions are critical in designing firm’s processes, but they cannot be answered only with static data provided even by the latest ERP systems. More advanced tools are needed and luckily also available.

SCDigest: “If the analysis is done on spreadsheets, can you really factor in all the right variables, do you really have an optimal answer in the end, and what is your level of confidence you have telling executives what should be done?”

We see simulation modelling as one of the most exciting methods in advanced business intelligence for logistics. It can offer highly customizable tools to design the supply chain with scientifically rigor approaches. When the simulation model is designed, utilized and interpreted properly the method can provide very detailed analyzes to support successful supply chain design.

A company with proper supply chain design system gains many benefits:

  1. Optimization of existing supply chain ranging from raw material to distribution of products. Also the possibility to deeply understand the firm’s logistic operations is eye-opening.
  2. Performing different scenario analyzes to test the firm’s system in changing business environment.
  3. Speed up the firm’s ability to react to changing business conditions such as costs, demand, etc. Also the risks related to delivery within the logistics chain can be tested.

Do your company a big favor and find out how your supply chain design could benefit from the advanced BI tools available. You’ll be amazed.

Undetected Hidden Biases in Data Lead to Poor Decisions

Workplace with computer with young businessman standing near by

The discovery of big data has reached nearly epiphany scale. Such hype around a new idea makes many people blind for the traps hidden behind the great potential.

Harvard Business Review has lately published many insights regarding big data in their blog network. The latest take on this hot topic by Kate Crawford discusses the hidden biases in big data. Crawford describes examples where big data originated from consumer mobile devices with social media features contains hidden biases caused by social and ethnographical factors. In such cases the generalization of results evidently leads to misinterpretations even if the data first seems comprehensive.

Hidden biases are not only an issue with big social media data but the same principles apply also to carefully collected corporate data. No matter how lavish your data is, it never makes the detailed understanding of its nature and shape redundant. In fact, quite the opposite. The more excessive your data is, the more accurately tailored your supportive tools need to be.

“As soon as the data was deeply understood, the model producing important predictions was created accordingly and the adaptive tool started to pay off…”

Our team worked with an industrial company that had recently deployed nice mathematical tools to refine their abundant data into demand predictions. These predictive analytics formed the foundation for operational management and decisions. The only issue was that the forecasts were far from accurate which led to poor managerial decisions affecting operational efficiency in over 30 countries.

The data was complete and the tools were based on rigor statistical methods so how could the derived predictions be wrong? Situation was frustrating as those predictions were the only meaningful information regarding successful decision-making.

There was a hidden bias in fundamental assumptions regarding distribution of the data. False assumptions made predictions inaccurate which directly led to uninformed decisions. As soon as the data was deeply understood, the model producing important predictions was created accordingly and the adaptive tool started to pay off. Decisions were supported by accurate analyses.

Specialized business analysts are able to tell you how reliable your data is for different predictive analyses. Once the biases in data have been detected, multi-method approaches are often needed to interpret and analyze the data accurately.

Don’t waste the excessive potential that lies in your business data. Take actions to first understand it thoroughly and build your predictive analytical tools on that. It will pay off.

Gartner Taps Predictive Analytics as Next Big Business Intelligence Trend

Focus on documents and pen on the table. Blurred people on background.
** Note: Shallow depth of field

Analyzes on firm’s internal processes and business environment are the driving engine in modern companies. This is moneywise evident in the large-scale investments that the big firms are still making to build new business intelligence tools. If the current growth continues, Gartner estimates that the total value of business intelligence business over triples by 2020 (current size estimated at 57 billion USD).

“Gartner proposes that in near future simulation based models are required to make more sophisticated predictions…”

The fastest growing subclass in business intelligence is data discovery or more generally large data analysis tools. These tools give an opportunity to describe and understand what the data implies. The current trend is that these tools are developing towards more visualized and illustrative ways to present the datasets.

Predicting the future means looking forwards – This is also the future of business intelligence according to Gartner. The article identifies that the next breakthrough in business intelligence is predictive analytics, which will increase the accuracy of predictions. Currently used methods such as extrapolation enables predictions to certain accuracy, but Gartner proposes that in near future simulation based models are required to make more sophisticated predictions. Gartner foresees that having an efficient way to utilize big data in predictions and then building decision-making on them is a critical component in building foundations for firm’s sustained competitive advantage in the future.

It’s hard not to agree.

Read the full article HERE.