Do you know how easily your organization’s business intelligence can turn into a lost opportunity? Well, it is just a matter of TIME. Intelligence is of value if, and only if it arrives on time.
In today’s hyper-competitive market environment, business intelligence continues to be an area of investment and interest for businesses. The ability to turn raw data into meaningful and useful information that can impact business performance is a powerful value proposition.
Despite the emergence of new devices and software products designed to unite employees in more ways than ever before, the threat of organizational silos is still very real.
Lack of collaboration between individuals and teams in different departments working on similar assignments and projects could ultimately lead to inefficiencies and loss of productivity.
The best-case scenario for this duplication of data analysis is that teams come up with the same result. When individuals or teams produce different numbers this cause disagreements about who had analyzed the ‘correct’ data and which can be fully trusted.
In the grand scale of things, this distorted view of data can be devastating, but there are even more ways that data silos can put your business in danger.
Keeping a pulse on business’ sales, marketing, finances, web analytics, customer service, internal R&D, IT, and more as isolated sources of data will never give a complete picture.
The scary truth is big data doesn’t lead to big insights if you can’t bring it together.
Yet many businesses are only scratching the surface of what’s possible with business intelligence.
To realize the potential of business intelligence and take its value to the next level in your organization, you need a solid understanding of where you are, what you want to achieve, and what’s possible.
From generating reports and charts that depict business performance, to implementing a truly transformative solution that uses powerful advanced analytics to not only predict behaviors and outcomes but to prescribe recommended courses of actions, business intelligence can be a strategic weapon that significantly impacts your bottom-line.
The next breakthroughs in business intelligence (BI) and analytics will see machine learning and artificial intelligence used to improve data access and data quality, uncover previously hidden insights, suggest analyses, deliver predictive analytics and suggest actions.
BI and analytics vendors are developing “smart” capabilities that will power the next step beyond self-service analytics, helping to further democratize data analysis for business users.
The benefits of business intelligence tools far outweigh the investments they entail. They can help businesses gain valuable insights to affect growth, resolve urgent concerns, collate marketing data more quickly, provide a real-time view of the organization and allow for the anticipation of future outcome using predictive and prescriptive analytics and forecasting.
Smart City, Smart People, Smart Data
Creating a smart city is based on concepts of innovation, technology, sustainability and accessibility ensuring economic progress as well as a higher quality of life. This is opening an infinite number of opportunities to become more efficient in both public and private management. It means that both the public sector, as well as the private (all types of business) sector have to be prepared to express their ambitions collaboratively about what they want to achieve in the future.
Democratisation of technology has meant that people are much more demanding, informed, über-connected and multi-channel. With the advent of new technologies in particular Internet of things, new business models are emerging to build solutions that increase or improve the citizens’ quality of life. from optimising public transport routes to using smart garbage bins to track litter habits.
Whilst the deployment of smart cities involve several innovative technologies to facilitate sustainable urban spaces, the concept is still vague and open. The ‘smart’ capabilities need to be operational and measurable. In order to evaluate how ‘smart’ is a smart city, robust data management and analysis is required.
This entails very close collaboration between both public and private sectors to share and analyse the vast amount of data being generated by new technologies. There are a billion places to gather data, and more tools are coming to market to help collect as much of it as possible.
The ability to share vital information in real time would enable businesses operating both in the private and public sector to develop powerful hardware systems and software solutions; that not only support automation but provide the ‘smart’ capabilities of a city and its infrastructure. Today, there’s an assortment of technologies being used to handle various characteristics, such as high volume, data location, and a variety of data source types. The collection of crucial data from any kind of source, such as the own city’s sensors, participatory sensing (for instance, sensors integrated in citizens’ smartphones), would enable the compilation of information about people and vehicle traffic, parking, environmental values, waste generated, energy consumption and healthcare etc. for the smart functioning of the city’s basic services.
It’s easier said than done one is tempted to say. Whatever the hype, whether artificial intelligence, machine learning or automation, it must start with data. Data is vital for smart cities technology.
First and foremost sound and mature data management practices need to be in place. Technology alone is not sufficient to build a smart city. Competent human intelligence is also part of the equation to complete this: Employees need to be comfortable analyzing and making decisions with data. Not only should the data analytics platform be robust, the team’s responsible for it must have a good mix of skills. The tecchies and fuzzies of this world will drive the vision of the Smart City not the traditional analysts.
“Finding solutions to our greatest problems requires an understanding of human context as well as of code; it requires both ethics and data, both deep thinking people and Deep Learning AI, both human and machine; it requires us to question implicit biases in our algorithms and inquire deeply into not just how we build, but why we build and what we seek to improve.” * (Scot Harley )
The essential question in the continuously growing amount of data volumes is how to make practical use of these volumes and without analytics, interpretation and algorithms it just isn’t possible. Advanced analytics has emerged as a critical component of modern analytics architecture, with companies turning to statistics, predictive algorithms, and machine learning to maximize the value of very large data sets. Without having to examine every dimension and variation in the data manually, people are automatically guided to relevant insights and alerted to data points that are worth exploring. The use of AI-driven smart data for customer analysis, fraud detection, market analysis, and compliance is becoming a reality to uncover insights hidden in data.
Investing in a strong modern analytics platform leverages the partnership between Business and IT . When business users are given tools to analyze data on their own, they are free to answer questions on the fly, knowing they can trust the data itself. This leads to accurate, agile reports and dashboards and one single version of truth. And IT, free from dashboard and change requests, can finally prioritize the data itself: safeguarding data governance and security, ensuring data accuracy, and establishing the most efficient pipelines for collecting, processing, and storing data.
Adapting to a scenario that is extremely technologically, economically and socially dynamic is the lynchpin of development and helps to drive smart systems geared towards improving integration and interaction of the smart citizen.
When data is approached intelligently to generate insights into how the tech systems are performing it is only then that efficiencies and savings could be measured across all strategic elements of Smart Cities -enterprise competitiveness, mobility, urbanism, energy, water, waste recycling, security, culture and healthcare.
Tableau helps people see and understand their retail data no matter how big it is, or how many systems it is stored in. Quickly connect, analyze, and share insights to reveal hidden opportunities that impact each sale, and your entire organization. With a seamless experience across PC, tablet, and smartphone, ask and answer deeper operational questions with expressive, interactive dashboards—no programming skills required.
Key Benefits of Tableau in the Retail Sector:
Tableau can help retailers to solve the problem of having the correct product, at correct inventory levels, in the correct stories. By analyzing product availability by category, supplier, day, and store region, retailers can identify gaps in efficiency and interactively drill into the details.
More than half of all retail sales are made during promotions, so it’s important to be able to visualize data during each phase – pre-promotion, the promotional period itself, and post-promotion. Tableau helps you create dashboards showing exactly what’s happening throughout the promotional cycle so they can prepare for availability issues by highlighting potential inventory stock shortages.
Store operations dashboards can provide rich insights to the corporate office, and region/ store managers about performance and execution. With store operations dashboards, managers are able to better benchmark the performance of stores in a region against metrics like Sales, Year Over Year growth, Traffic, Average Transaction Value and Units Per Transaction. They can also drill down to unique store level operations data like weekday vs weekend sales, product department performance, compare space productivity within the store and evaluate top selling brands.
Category managers need to make profitable decisions around products that customers are demanding. In order to do that, merchandisers need to quickly analyze demand data by sales and profit margin performance by departments, sub-categories and brands. With merchandising assortment dashboards, managers can gain a quick overview of relative sales performance of sub-categories, share of private label and national brands, and categories that can contribute up to 80% of sales. They can also get richer insights on the attributes that lead to higher conversion such as product type, colors, shape, and styles.
For most consumer product goods companies, brand perception is critical to the success of their business. Executives at large CPG companies need insights into how consumers perceive the company brand, for the benefit of derived utility and price.
A brand health dashboard can show the benefit vs price effect by deep diving at the sub brand level . Combining this with profitability and price elasticity of the brand, executives can decide on allocation of additional marketing dollars to gain Category leadership for these brands and push the benefit perception higher for higher profitability brands.
Happy to have a chat with you to share ideas, discuss opportunities or even prepare a demo for you . Contact us on firstname.lastname@example.org.