As businesses continue to accumulate data at a lightning pace, they need advanced analytics to manage it and bring out relevant insights with speed.
Advanced analytics uses machine learning and data science algorithms to identify patterns in massive volumes of data, correlate data points, and bring out trends and forecasts.
Right from predicting sales volumes to demand forecasts, the possibility of machine downtime, and customer sentiment, advanced analytics is being applied to all types of business operations today. In-depth insights derived from advanced analytics are enabling businesses to improve their current offerings, enhance customer experience and also explore the newer potential in an informed way.
To able to get the most out of advanced data analytics, it is critical that businesses adopt the right processes. Here’s a list of some best practices that you can follow to get the best outcomes from advanced analytics:
Figure out the right data
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Every business has piles of data, but they always do not know what data they should comb to get insights that would be most impactful for them. They often clarity on how this data and insights can be imbibed into day-to-day decisions. A good way to get started is by focusing on data that help in answering immediate business problems. For example, if there’s a continuous drop in sales of a certain product, sales teams can combine data from all POS systems across locations, look at customer interaction data, correlate it with pricing, discounts, and marketing campaigns, and get to the bottom of what is really causing the decline. They can also get recommendations on corrective actions that they need to take for improving sales.
Data is being captured by new mechanisms every day. There’s social media clutter that covers customer sentiment and feedback, then there is data from internal applications like CRMs, ERPs, and more. Data is also being captured by sensors and other devices tracking customer movement. A business can collate all of this information and get futuristic insights. By knowing how a customer traversed through their store and what products they picked, versus the pricing preferences of that customer, a retailer can get an accurate prediction of what discount to offer to that customer to ensure a purchase. Sometimes businesses do not realize that they have data points that can be vital in improving their customer experience or revenue. Advanced retail analytics helps them to bring everything together and get the right insights in real-time.
Prepare the IT teams
Enroll the IT teams in the transformation and involve them in building new architectures for advanced analytics. This will involve moving away from siloed applications and streamlining unstructured data which is difficult to manage with traditional IT capabilities. Typically, legacy systems deliver data in batches. For adopting advanced analytics, IT teams need to figure out platforms that support the continuous flow of data and insights to business users.
Get the models right
Build models based on your business needs, and not data available. The priority for defining data models should be to enhance performance and optimize outcomes, along with accurate predictions. Businesses must have clarity on what data models will help them to improve efficiency at all levels and then identify data points that should be analyzed and correlated for this outcome.
Correlating multiple data sets enables businesses to run different algorithms and statistical analytics to unravel hidden patterns and trends in their data. Advanced analytics enable businesses to mine their data efficiently and harness powerful insights to optimize day to day operations as well as long term strategies.
Keep it in sync with business objectives
For any advanced analytics project to be successful, it is essential that it is in complete sync with what the business wants to achieve. It must be tightly integrated with day to day operations and decision-making processes. Having an understanding of crucial processes helps in setting up advanced analytics in the right direction. For example, by knowing all the parameters and processes that a product manager uses to determine the pricing of products going out in the market, models can be built to predict the best pricing combinations for a given time duration. The product manager would know what pricing will work best in the next week, month, and one year down the line. They will also get recommendations on what parameters they should tweak and by how much to sustain profitability.
Integrate into existing apps
Businesses run multiple applications at a time. From payroll to ERPs to CRMs, there are different tools being used across different departments. To make advanced analytics most efficient and ensure higher adoption, businesses can embed advanced analytics into their existing applications. This would mean that the users will not have to switch between multiple windows to view insights and alerts. Everything would be accessible to them within the same application, which they are accustomed to. Since the users are comfortable with certain user interfaces and advanced analytics is made available to them on the same screen, they can able to quickly adapt to it and use it more effectively.
Benefits of Advanced Analytics
The foremost advantage of advanced analytics is being able to deliver accurate forecasts for businesses. This really empowers businesses to make futuristic decisions in an informed way. Advanced analytics takes the guesswork and anticipation out of the business processes and transform them to be more scientific and data-driven. Advanced analytics bring proactivity to the whole business structure. People are better equipped to pro-act on situations rather than passive reactions.
Advanced analytics help businesses in mitigating risks. Users get alerts and notifications on impending downtimes or threats and can proactively take actions to avert the threats. Businesses can not only control current fraudulent activities but also foresee future risks and can take steps to track the perpetrators. They can also modify their risk management processes in advance to reduce such instances in the future.
Advanced analytics enable businesses to identify the right product lines. By collating data from multiple sources and analyzing it from different dimensions businesses get a deeper understanding of what products are likely to be more successful in the future. They can shift their production, sales, and marketing strategies to align with these forecasts and deliver products in time to meet the high demand in the market.
To conclude, it would not be an exaggeration to say that advanced analytics empowers businesses to clear confusion and avoid conflicts by becoming more fact-based and data-driven in their operations. Advanced analytics enable businesses to prepare for the future scientifically by delivering future-oriented insights every day.