Here's how you can harness technology and data analytics to enhance business insights and decision-making.

They play a crucial role in data analytics . Optimizing cross selling strategies and detecting frauds or future market behaviours is so so important and needs to be taken care off

Data analytics plays a crucial role in enhancing business insights and transforming raw data into actionable recommendations. There are some strategies to harness technology and data analytics for business growth like - personalize the customer experience to fuel business expansion. Optimize operational efficiency to streamline processes. Leverage Predictive Analytics models to help anticipate trends and enable proactive decision making. Most importantly foster a data-driven culture across the organization.

The first objective is to begin eliminating data debt. Data debt occurs when we have multiple data lakes, nonstandard means of capturing data, and inconsistent data curation. Create standards around these processes first before you begin this journey to eliminate data debt.

Understanding your customer and their needs is critical in today’s world. Data mining to understand the needs and wants is a key to better customer service. Using data and polling customers with key KPIs helps organizations pivot and incrementally improve the customer experience. Knowledge give you the power to change and act for better outcomes.

Data is an invaluable asset essential for making informed decisions at an organizational level. Harvard Business Review has likened data to the "new oil" of the 21st Century, underscoring its importance. The process of preparing decision-making data involves data mining from diverse sources and subsequently integrating it into databases such as SQL and Tableau. Following extraction, the data can be efficiently organized and presented using tools such as Excel and Power BI to ensure its comprehensibility and utility.

Although power BI is a good visualisation tool, but requires skills. The age-old warhorse has been excel which is useful to club data the way you want it. Although tools are important but what's more important is the logic, segregation of the cause and effect and then using the right data for the problem identification

The right analytics tools can transform raw data into visual, easy-to-understand reports. These tools often feature dashboards that provide at-a-glance insights, crucial for quick decision-making. You can track key performance indicators (KPIs) in real-time, enabling swift responses to market changes or operational inefficiencies. Regularly reviewing these insights ensures your business remains agile and adaptable to new challenges and opportunities. By leveraging analytics tools, you enhance your ability to make informed decisions, optimize performance, and maintain a competitive edge.

Implementing the right analytics tools has transformed how we approach decision-making. These tools convert raw data into visual, easy-to-understand reports, often featuring dashboards that offer at-a-glance insights. Tracking key performance indicators (KPIs) in real-time has been particularly beneficial, enabling us to respond swiftly to market changes or operational inefficiencies. Regularly reviewing these insights has kept our business agile, allowing us to adapt quickly to new challenges and seize emerging opportunities. This proactive approach has significantly improved our overall efficiency and competitiveness.

The three most useful considerations when defining use of analytic tools involves: 1) Data-Driven Strategy: Leveraging advanced analytics tools to turn raw data into visual, easy-to-understand reports enabling quick decision-making and identification of market trends and customer behaviours. 2) Financial Acumen: financial modelling and forecasting provides precise analysis of financial statements, budgeting, and investment opportunities to ensure sustainable profitability and risk management. 3) Operational Efficiency: Enable skill development in process optimisation and performance metrics, implementing best practices and technologies that streamline operations, reduce costs, and enhance productivity.

Every section of this article resonates with me as I have been with a database marketing and predictive analytics firm called AnalyticsIQ since 2010. We make our living from analyzing historical data, performing predictive analytics, and leveraging this to help clients make informed marketing decisions. To me, predictive analysis is tons of fun and we do it everyday. What I have learned over the years is that this is the ‘easy’ part. The difficult part is the data preparation and all of the pre-processing that must occur prior. The best analysis will fail miserably if the data is flawed. Like with most things in life, the real work is in the preparation.

In a lot of ways, predictive analysis enables evidence based results based on the hypothesis one is establishing by experimenting various actions to achieve an output by extrapolating the results. It is an imperitive measure by which one can take a corrective action on the current activities by controlling the "delta", analyzing forecasted future outcomes to refine the immediate tactical goal and align with the long running strategic goal.

With emerging storage and processing power, usage of AI/ML/Big-data/Data-pipeline/Data-lake etc has become common in the quest of optimization and making the products and services user friendly. Predictive analytics is of the powerful tools which can be used for Supply-chain optimization, customer requirement analysis, competitor analysis, and almost anything. The predictive analytics models can be classified under 5 broad heads [choose wisely ;)] : 1. Regression Models: Linear, Logistic, Polynomial etc. 2. Classification Models: K-Nearest Neighbors, Decision Tree, Random Forest 3. Time Series Models: ARIMA, 4. Cluster Models: K-Means, DBSCAN 5. Neural Network Models: Artificial Neural Network, Recurrent Neural Network etc.

Predictive analysis leverages historical data to forecast future outcomes. By applying algorithms and statistical models, you can anticipate customer behavior, sales trends, and potential operational bottlenecks. This proactive approach enables you to prepare for various scenarios, allocate resources more efficiently, and mitigate risks before they become problematic. Predictive analysis is a powerful tool for strategic planning, offering a forward-looking perspective that informs and shapes business decisions. By integrating predictive analysis into your workflow, you enhance your ability to stay ahead of trends, optimize operations, and achieve long-term success.

Using predictive analysis has significantly enhanced our strategic planning. By leveraging historical data and applying advanced algorithms and statistical models, we've been able to forecast customer behavior, sales trends, and identify potential operational bottlenecks. This forward-looking approach allows us to prepare for various scenarios, allocate resources more efficiently, and mitigate risks before they become issues. Predictive analysis has become an indispensable tool for our business, providing insights that shape our decisions and help us stay ahead in a competitive market.

One of the ways to identify insight from data is to first understand what are the various issues faced at the business level. Thereafter identify the data which corresponds to these decision i.e. is either a cause or effect. Then analyse the data to go deeper to identify basis segmentation ( doers v/s non doers), and various other customer types or consumption patterns

Instituting a foundation that connects altogther the DATA and modulirize the analytics findings in a systemic manner to the key business objectives, whereas results are fed into a continious improvement cycle of progressive actions I.e. for every module of findings, an eco system has got to integrate the findings into its program of work and accordingly to feedback the analytics system with its plans or course of actions Here then the analytics system is to be updated and feedback again in "a continious imporvement cycle"

The more (reliable) data you can access the better, but the risk is to fall into the trap of being too quantitative. The quantitative data should be your bedrock - your proof and your evidence, but the real insights should be qualitative, and gained from continuous interaction with your customers. Make sure you have strategies and channels for both.

Understanding your customers is crucial for business success. Data analytics provides deep insights into customer preferences and behaviors. With this information, you can segment your customer base, tailor marketing campaigns, and personalize the customer experience. Analyzing customer feedback and engagement helps improve product offerings and customer service. These insights are invaluable for building customer loyalty and driving revenue growth. By leveraging customer data, you can make informed decisions that enhance satisfaction and foster long-term relationships with your customers.

Les données d'analyse de bases publiques permettent de disposer d'information macroéconomiques par secteurs d'activité, utilisables aussi bien pour les études de marché que pour une utilisation interne, dans la production de ratios de comparaisons avec ses propres indicateurs.

I like to start from the end and work backwards. What is the ideal outcome? What is the desired future state? The answers to these questions then inform what data to access or collect, how to analyze it, and how to convert this analysis into valuable solutions for the business.

Prcoess optimisations with automations not only add efficiency but can also help boost productivity with cleaner and relevant details upfront for analysts and leaders to focus directly on problem statements. Not to forget about cost savings the above add.

One great thing about process optimization is that it reveals redundancies. Now adding data analytics to that flow, and you'll have much more efficient operations. Remember, "whatever does not add value is waste."

El aprovechamiento de la tecnología y el análisis de datos en la industria de alimentos permite optimizar la cadena de suministro, mejorar la calidad y seguridad alimentaria, y desarrollar nuevos productos alineados con las preferencias del mercado. Además, facilita la personalización del marketing, incrementando la eficacia de las campañas y la relación con los clientes. Estas tecnologías también promueven la sostenibilidad y eficiencia operativa mediante el uso óptimo de recursos y la reducción de residuos. En conjunto, permiten una toma de decisiones basada en datos concretos, mejorando la precisión y efectividad de las estrategias empresariales.

You can tell an expert business management consultant if they can demonstrate how to leverage technology and data analytics to optimise business operations, and ensure maximum efficiency. They can meticulously analyse workflow data, identify bottlenecks and flag areas for improvement. This approach allows the organisation to then streamline processes, reduce waste, and boost productivity. The results are two-fold: cost reductions and enhanced product or service quality, leading to higher customer satisfaction and improved market positioning. Ask the management consultant to show you their expertise and that will ensure your business will not only operate efficiently but also stay competitive and agile in a dynamic market.

A lot of these aspects can build off each other and combining them provides a "multiplier effect". An example from a previous implementation that I led included deep probing of data (data mining of each record) in transit, detecting and comparing with previous patterns of flow of similar data set flows and finding anomalies. Based on the anomalies detected, risk management dashboards would light up and experts in 3 different risk areas would be alerted and if required could take action.

To harness technology, AI, and data analytics for enhancing business insights and decision-making in risk management, implement asset management systems and for real-time inventory and monitoring, and use analytics tools for asset valuation and criticality assessment. Leverage AI-driven threat intelligence platforms to aggregate and analyze threat data, and deploy tools such as automated vulnerability scanners to identify system weaknesses. Utilize predictive analytics to assess the likelihood and impact of potential risks. Finally, align mitigation strategies with business goals by integrating data insights into decision-making processes, ensuring responses are both effective and strategically sound.

Risk is fluid. It changes all the time and so needs to be a habit rather than an event. Data analytics is crucial to risk identification as it can highlight trends before they are noticed. It can also help with risk mitigation, as that is all about 'getting the biggest bang for your buck' in where you spend time and effort to reduce the most amount of risk. Use datasets, then, to inform as well as prescribe - and, crucially, you will know if your mitigation has worked if the data improves.

Without adequate expertise in the subject area, even the best tools will not identify unexpected risks at an early stage so that the correct mitigation measures can be executed. Ideally, highly experienced experts can develop a risk management plan before a project is launched - with suggested mitigation measures that can be quickly deployed when needed. Most vital is to have a trained eye overlooking the project. All too often, experienced experts are left out of projects I order to "save" on the project budget. The result is often a huge budget overrun!

Onderstanding the degree of optimism bias in your culture / organisation is an important factor in analysing risks and producing risk mitigation strategies. If you cannot answer the question "how well did we predict the liklihood and impact of this type of risk previously" you are not applying a continuous improvement approach and that is not best practice.

Modern Businesses are all Digital and Technology enabled businesses. The Technology availability has led to business information, numbers, facts and figures which were hitherto within multitude of paper and paper registers , be available in a form that can be stored, can be operated upon with mathematical, statistical and information processing tools / algorithms. •The use of such technology and mathematical, statistical algorithms and computational analysis of business data and information helps businesses to achieve: •Better Decision making •Save Time and Money •Understand Markets globally and not restricted to particular geography •Stay Ahead of Competition •Manage Risks and Governance

Data visualization is important too. Work on developing interactive dashboards that visualize key metrics and KPIs because they allow stakeholders to quickly grasp insights and trends, facilitating faster decision-making.

Harnessing technology and data analytics can significantly enhance business insights and decision-making. By leveraging advanced analytics tools, businesses can uncover patterns, trends, and correlations within their data. Real-time data processing and visualization enable quick, informed decisions, while predictive analytics help anticipate future trends. Integrating AI and machine learning models can further refine accuracy and efficiency. Utilizing these technologies ensures a data-driven approach, optimizing performance, and driving strategic growth.

Fundamental to any data and analytics strategy is ensuring the quality of your data. It is not a 'sexy' activity, but without it the resulting insight is unlikely to deliver the desired value. Remember, rubbish in, rubbish out. Do the basics brilliantly and have a clear data/analytics strategy that is customer focused and fully aligned to the overall business strategy.

Wars occur in decades but every day is a business war & a race of survival in the world of business policy. Data processing , applying analytics , evolution of trends , conflict detection , redundancies, resource cross overs and what not . Draining all this is to arrive at the amending and developing business strategy and tactics . Optimization saves , it pays and keeps businesses afloat in cut throat dynamic market competitions . Selection of analytical tools , methodologies , negotiating trends , figuring out futuristic strategy and managing present bottle necks prior to occurrence ; further facilitates decision making . Effective decision making and having a business plan is dependent upon the basic blue prints of information systems .