2nd of May , The team participated in a talk on the era of smart data organised by the MIOD Fellows Networking Event. Great night exchanging ideas on data analytics , the future of BI and the tools needed to move forward with data strategy.
Expand Horizons, Change Attitudes!
International Girls in ICT Day is celebrated every year on the fourth Thursday of April and was celebrated on 25 April 2019.
This year Business Lab Consulting is the proud sponsor of Girls in ICT Day in Mauritius. We participated in the workshops to bring awareness of the power and possibilities a career in technology can offer to young girls and women across schools, universities, ICT companies and government offices.
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.
Salesforce revolutionized Customer Relationship Management (CRM) by leveraging the power of the cloud. Tableau puts that rich customer data to work by providing analytics for everyone in your organization.
Blend Salesforce data with other types of data for a deeper understanding and increased visibility into opportunities. With Tableau, you can provide your entire sales force, channel team, and executives with secure, up-to-date, customized views of data—even through browsers and mobile devices.
When it comes to sales metrics, everything from your pipeline to your revenue is mission critical—so your analytics solution needs to be fast, easy and talk seamlessly with your entire data ecosystem.
Saving time in every step of your sales data workflow is fundamental. From accessing and analyzing complex data sets, publishing interactive dashboards, and sharing across your organization, for your sales data to be impactful, the insight-to-decision process must be swift.
Use these 5 tips for boosting your pipeline with Salesforce data:
1. Connect to Data Everywhere
2. Blend Salesforce Data with Other Data
3. Answer Big Questions with Better Visualizations
4. Use your Data in the Field
5. Put Data Findings Everywhere
Nobody understands decision-making better than sales people. In today’s marketplace, how fast is the speed of business, actually?
The hallmark of data-driven sales teams is the ability to see and understand data analysis at the speed of thought—allowing for the asking and answering of questions as fast as salespeople and business leaders can think of them, even against multiple large data sets. What number can you hit? What deals can you close? What regions matter most today? Don’t limit your decision-making; for maximum results, sales performance demands flexibility and finesse from their people, their processes and most critically, their data.
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.
Every customer touchpoint generates data—and that data reveals a lot about what matters to your customer. Be ready to lead with data analytics and improve the customer experience across every channel. The infographic below provides a glimpse of what future-facing banks know about the importance of customer experience and analytics in banking.
If you’ve ever analyzed data, you know the pain of digging into your data only to find that the data is poorly structured, full of inaccuracies, or just plain incomplete. But “dirty data” isn’t just a pain point for analysts; it can ultimately lead to missed opportunities and lost revenue to an organisation. Gartner research shows that the “average financial impact of poor data quality on organizations is $9.7 million per year.”
The amount of time and energy it takes to go from disjointed data to actionable insights leads to inefficient ad-hoc analyses and declining trust in organizational data.
A recent Harvard Business Review study reports that people spend 80% of their time prepping data, and only 20% of their time analyzing it. And this statistic isn’t restricted to the role of the data stewards. Data prep tasks have bled into the work of analysts and even non-technical business users.
Enterprises are taking steps to overcome dirty data by establishing data hygiene etiquette:
Some people refer to this process as “data discovery” and it is a fundamental element of data preparation. Confusion around data definitions, for example, can hinder analysis or worse, lead to inaccurate analyses across the company. For example, if someone wants to analyze customer data, they may find that a marketing team might have a different definition for the term“customer” than someone in finance.
This will help analysts understand how terms are used within each business application, showing the fields are relevant for analysis versus the ones that are strictly system-based. Developing a data dictionary is no small task. Data stewards and subject matter experts need to commit to ongoing iteration, checking in as requirements change. If a dictionary is out of date, it can actually do harm to your organization’s data strategy. Communication and ownership should be built into the process from the beginning to determine where the glossary should live and how often it should be updated and refined.
You need to prepare your data before even thinking of importing it in your system. Every organization has specific needs and there is no ‘one size-fits-all’ approach to data preparation. A self-service data preparation tool allows people to see the full end-to-end process, seeing potential flags earlier on—like misspellings in the data, extra spaces, or incorrect join clauses. It also increases confidence in the final analysis.
Keeping your hands out of the data in regular use increases the chances of it keeping clean. Introducing a little dirty data to a system will compromise an entire data set and your little bit of dirty data has suddenly created a lot of dirty data. Cleansing the mess is a far far bigger job than making sure the data is clean before importing it.
Adopting a self-service data prep across an organization requires users to learn the ins and outs of the data. Since this knowledge was historically reserved for IT and data engineering roles, it is crucial that analysts take time to learn about nuances within the data, including the granularity and any transformations that have been done to the data set. Scheduling regular check-ins or a standardized workflow for questions allows engineers to share the most up-to-date way to query and work with valid data, while empowering analysts to prepare data faster and with greater confidence.
Data hygiene should be a top concern in organisations. Devoting some resources to ensuring that the data you’re basing decisions on is complete and accurate is a smart investment, because dirty data is costly in so many ways. To get the most and best use out of your data, you need to take the time to ensure its quality is sufficient and that data used by different departments is integrated. This gives you the most complete and precise customer view, so you can make smarter decisions and maximize your return on investment.
Data and analytics are playing an increasingly critical role in hotel and leisure operators’ understanding of their customers’ behaviour, so that swift actions can be taken to really satisfy their needs and wants.
At the same time, hotel operators have multiple datasets lying in various locations – customer profiles, customer feedback, occupancy rates, F&B sales etc. – creating data silos, from which the fullest potential cannot be tapped until they are integrated to gain a single version of truth.
Your teams could use Tableau to develop weekly and monthly reports that they could share with the entire company. The reports could feature sales figures and updates from top management and country managers, marketing and financial data, along with other growth updates. The comprehensive dashboards make sharing complex information with the rest of the company a much easier task.
With weekly reporting that details KPIs and shows how booking patterns are changing, details on revenue, commission and conversion rates but to name a few, allows you to extract the best insights for the team.
Tableau helps you stay competitive by making the time to develop and deliver insightful analysis and reports significantly shorter.
A business, which is highly metric-driven, is optimized with Tableau’s rapid-fire data analytics and drag-and-drop functions as period-on-period metric can be compared.
Tableau is a powerful tool because of its cross-platform adaptability. Tableau’s beautiful dashboards can be mobile-optimized, which makes it easy for everyone to access the findings even on the go. Tableau basically has the power to give you any insights you are looking for from the data that you can get your hands on.
Tableau can be a huge game-changer. So instead of having to wait for something to already happen and then try to figure out why it happened, you can now proactively look out and see what’s going to happen before it happens and then prepare for that or maybe change it.
