Adopting a lean strategy can seem intimidating, but you can easily create a successful company using lean methodology. Initially developed by Eric Ries, the three-step lean method for startups uses iterative product testing plus integrates early adopter feedback to choose product features and functions with the goal of reaching a larger market. Using lean tools can help eliminate constraints, roadblocks, and waste that may impede progress.
Your organization can benefit from analytics regardless of business type. You can increase the benefits by making your analytics information easily accessible and understandable to all. You don’t need to teach your entire organization statistics and statistical analysis for the business to benefit from analytics. You can set up the back-end of your data ecosystem to do all the hard work and present the information on an easy-to-read, simple-to-understand dashboard that presents the information in digestible chunks.
Data storytelling is an increasingly important competency within Data Science. Data Storytelling not only makes insights engaging but also makes them significant for the audience. Good data storytelling has become even more important today as evidenced by the insights conveyed about COVID-19. The pandemic has created the perhaps the most populous quest for data and statistics in our lifetime. It has highlighted the importance of the ability to convey insights from data in a way that is meaningful to people’s everyday lives. That same principle can be applied to business decision-making and the need for insights in support of it.
According to the Harvard Business Review, 70% of employees are unable to select their own company’s strategy on a multiple choice quiz. As employee interests and company interests fail to combine, companies themselves become disconnected. Employee engagement is incredibly important to the health of a business. But how can it be improved?
By: Sharon Mattei
John Tardy is a principal consultant at Starr & Associates and leads the Business Intelligence and Data Analytics practice area. He has a BS in Electrical Engineering and an MS in Biomedical Engineering from Rutgers University and an MBA in Management of Technology from Georgia Tech. He has worked with start-ups and served in senior leadership roles with some of the most recognized brands in the country. He pairs a depth of technical expertise with business savvy to deliver practical and impactful solutions for his clients.
Organizations across the world are finding themselves with increasingly complex supply chain processes to manage. While in the past most commercial enterprises were local, modern enterprises cannot survive in this way. Many modern businesses are finding themselves having to develop new technologies to manage these strategies — one of which may become the blockchain.
Current supply chains are still using management and logistics processes that were developed a long time ago. These are very streamlined processes that involve a small number of components. But supply chains are operating internationally now; even organizations that only deal in local trade are often sourcing their materials from another country.
This introduces many logistical concerns. Supplies and assets must now change hands a multitude of times, shifting responsibility from one organization to the other. Supplies need to be able to be tracked by multiple vendors and businesses, which can be difficult when they are traveling such tremendous distances. Blockchain technology may be able to help.
Blockchains provide a completely transparent and consolidated transaction sheet, which can comprise a multitude of different transactions and verifies each transaction with all other users of the system. In the blockchain, a transaction can be logged and then immediately propagated throughout the system, carrying with it information about the transaction and verifying that the transaction did take place.
As a consolidated but decentralized system, blockchains provide for superior security as they cannot be controlled by any one individual. Data cannot be lost because this data is held by anyone who uses the blockchain, and consequently suppliers don’t need to worry about issues such as securing and managing their own transactional data.
All transactions can be traced easily back to the original source through blockchain, and each entity in a blockchain can be tracked through multiple transactions. This has opened the door for companies such as diamond companies to track their individual assets throughout the entire supply chain, from initial mining to customer purchasing. Not only does this heighten security and streamline logistics, but it also provides vital data throughout the customer purchase process.
The blockchain is a relatively new and disruptive technology, but it has been around long enough that many companies are now seeing its true value within their organization. Through the blockchain, supply chains can enter into a new generation of scalable, consolidated, and decentralized solutions.
Companies will be able to track extremely complex supply chain management protocols with complete transparency and organizations will be able to work together with transparent and easy to access data. All of this will reduce supply chain overhead and costs and make it easier for companies to thrive. For more information about business intelligence and data analytics, contact the experts at Starr & Associates.
Many businesses rely on data analytics to help them improve customer satisfaction and lower their costs. But when paired with artificial intelligence, or AI, data analytics can further help your business. Here is some information about data analytics, artificial intelligence and how they can work hand-in-hand to benefit your business.
Data analytics are pieces of data that are used by managers, business owners, and marketing managers to help them determine their customer base and how to grow their business. Data analytics can show you who visits your business, who is interested in your product, what price point your product can best sell at, how engaging your advertisements are and who your target customer should be.
Artificial intelligence takes your data analytics and kicks it up a notch. Much of your data is achieved through actual testing. For example, if your target market is females aged 18 to 25, you may have tried to market to those who are younger and older to see what their interest was and failed. This helped you determine that those in this age group are most interested in your services. Artificial intelligence uses predictive behavior to predict that those who were older or younger wouldn’t have been most interested in your product. Ultimately, this predictive behavior helps the trial and error that goes into the pricey and time-consuming testing that goes along with data analytics.
Data analytics and artificial intelligence work hand-in-hand because your data analytics are fed into the AI system to give a basis for the predictive behavior that AI is able to determine. Artificial intelligence relies on data analytics to provide a full and accurate assessment for your business.
The biggest benefits associated with using artificial intelligence in data analytics is the cost and time-savings. Artificial intelligence is able to more accurately predict consumer behavior based on your data analytics, helping to ensure that you don’t have to go through the trial and error process of testing new products or defining your target audience. This helps you to save both time and money, ultimately allowing your business to grow faster and more rapidly.
Are you looking to incorporate artificial intelligence in your data analytics in the greater Atlanta, Georgia area? If so, we at Starr & Associates would love to assist you. We understand how to use the tools of business intelligence, analytics, and data mining to improve process efficiency and customer service, while also creating effective strategies for improving the end-customer experience and lowering costs. Contact us today to find out how we can help you.
Data-driven cultures are far-reaching. Nearly every industry is currently being disrupted through data analytics, and the field service culture is no exception. Many companies are utilizing a combination of unmanned drones, sensors, and data analysis to make field service more effective and comprehensive. Here’s what you need to know.
What if you could predict when a system would go down? With big data, you can. Big data uses a combination of sensors and historical data to identify environmental conditions that could indicate a breakdown. This data isn’t a replacement for traditional field service techniques, but instead, it’s designed to augment field servicing. Big data can be used to tell field service technicians when there could be a critical problem, as well as to fine-tune the number of times technicians go out and when they go out.
Predictive field service models work hand-in-hand with the Internet of Things. Internet of Things devices are utilized to capture data in the field, and this data is analyzed. Big data isn’t magic — it requires tremendous amounts of data which is then used to view patterns. As an example, a certain heat signature might only occur just before an element breaks. These patterns can then be used to create a risk assessment for individual machines and equipment on the field.
A data-driven culture is a cost-effective, safe culture. Better data means technicians need to be on the field less and are on the field when they are most useful. Not only does this reduce field-related industries, but it also reduces the overall cost to a business.
Creating a data-driven culture begins with the right hardware and software systems. Companies must take care to outfit their on-the-field infrastructure with the right sensors and IoT devices and must utilize state-of-the-art software to capture and analyze this data.
Companies also need to change their core business processes to directly relate to and manage this data. The technology has to be integrated at all levels of their field servicing so that data brought in and analyzed has an ultimate impact on when service calls are made and how service technicians operate.
Integrating data into field service is the first step towards making a more effective, productive, and competitive environment. Companies can substantially reduce their overhead while also reducing their risk, by utilizing an ecosystem that is less likely to experience breakdowns, delays, or injuries. For more information about this type of solution, contact the experts at Starr & Associates.
Nearly every company goes through some organizational growing pains. One common development, departmental silos, occurs at some point. Successful companies break down a silo, a department that protects its information, keeping it from other departments. Silos cause growth breakdowns, so continued growth requires their eradication.
