Archive for January 2016

Big Data in Agriculture

Over the next few months, I'll be writing blogs on some non-traditional industries that use big data. I'm looking forward to sharing updates and information on how big data can be used in all industries, not just the ones we typically associate with technology.

Farming is something most of us take for granted. We go to the grocery store and pick out our food without giving much thought to where our food came from or what went into growing it. We think of small quaint farms where farmers plant seeds, ride small tractors and then harvest their crops. However, many farms in the United States rely heavily on technology and are turning to big data to help them become more efficient, cost-effective, and less environmentally impactful.

Today’s tractors not only use sensors to collect information that help with preventative maintenance, but tractors also have multiple computer screens and sensors on them that collect information from everything from nitrogen and pH levels in the soil to how far apart the seeds are. Farmers tend to use this information while planting, however, many farmers do not use the information they’ve collected after the fact.

Farmers are also using “precision farming” to help make farming more efficient. This technique can mean many things, but ultimately it means using information about the soil and crops in a specific area to maximize the output of the crop and minimize the production cost for a crop. Farmers can use this information for everything from identifying the best places to plant certain crops to how many plants per acre they can plant.

In the future, we can expect to see more farmers adopting precision farming and other big data techniques. The big data market for agriculture is expected to grow from a $2.4B industry in 2014 to a $5.04B industry in 2020 (Research and Markets Global Precision Agriculture Market 2014-2020) and with the population projected to grow to 9 billion people by 2050, farmers will need to increase outputs significantly to keep up with demand. We’re already seeing some very interesting ways that precision farming and big data solutions us can be implemented at larger facilities. For example, Gallo Winery recently implemented a system that takes satellite imagery of their vineyards and determines which plants are getting too little or too much water. The images are processed, analyzed and then the sprinkler that is connected to an individual plant is automatically adjusted to give it either more or less water.  Water consumption at Gallo Winery has been reduced by 25% since the system was implemented, the health and production of the plants has increased and the costs associated with workers manually watering individual plants has decreased.

The real power of big data will be when farmers start sharing their data with companies. In the past, farmers have been very hesitant to share the data they collect to corporations. Many farmers view the information from their fields as propriety and are worried that the information generated from their farms will be shared with commodity traders or other farmers. They are also worried that seed and equipment companies will use the information to sell farmers higher prices goods. However, seed and equipment companies need information from individual farms in order to improve their software and products so farmers can keep achieving the best results possible. In the next few years, I believe seed and equipment companies will start focusing on how to earn the trust of farmer and proactively show farmers how sharing this information will lead to substantial ROIs for the farmers. Also, as time progresses farmers will become more comfortable with big data and the technologies and realize that the payoff of higher yields and ultimately lower costs will persuade farmers to share their data.

Trends in Big Data and the IoT in 2016

As we enter the new year, it’s always an exciting time to reflect upon the previous year and ask “What new things will happen next year?” Over the past year, it’s been really cool to see how executives at companies are realizing the value of using big data instead of just collecting it.  Because of this trend, 2016 should bring about disruptive changes in the big data and internet of things markets.

Some of the top trends in 2016 that I see happening are

Customer satisfaction levels will be influenced by an automatic personalized experience

As consumers become more tech savvy and more millennials have discretionary income, more consumers will continue to adopt and use mobile apps such as Target’s Cartwheel or PriceGrabber while they’re shopping. These consumers are looking for a personalized experience that will give them some benefit, whether it’s a lower price or targeted advertising or coupons based on past behaviors, when shopping. Consumers have many options to choose from when shopping both online and in-person and will ultimately pick the store that gives them the most value and the best shopping experience.

Additionally, with the increase of internet shopping and the multitude of stores available to consumers, companies will start relying more on what an individual is clicking on and posting on-line about products and her shopping experience. In the past, companies have had challenges making sense of this information in a timely manner and then reacting. However, companies are starting to discover solutions that can help them not only react in real-time to a customer’s shopping experience but also personalize the customer’s shopping experience based on past behaviors or trends. These proactive actions should lead to higher level of customer satisfaction for the customer.

Using ROI in big data

Executives are pushing for the adoption of big data solutions however, many executives want to see a measureable ROI and meaningful use cases before they make a large investment in a solution. In 2016, solution providers will start partnering with their users to determine the ROI of using a solution. Many times these measurements can be straightforward, such as calculating how much revenue is saved when using data sensors to predict when parts will wear out.  However, calculating the ROI on other solutions that combine structured and unstructured data will be more challenging to determine.

Data in the Internet of Things will start to be used instead of just collected

Sensors on many devices will help companies predict when parts need to be serviced and can also predict anomalies in the overall system. However, many companies have yet to realize the full potential of this data. In 2016, more companies who collect this type of information will no longer just store it but start to use this information to help prevent down time and achieve better customer service. Also, with the increased adoption of personal healthcare devices, such as Fitbits and smart watches, more consumers are going to start tracking their own healthcare.  Companies that provide solutions that monitor and make recommendations on a consumer’s heart rate, blood pressure or fitness activity will grow.

The need for simplified Big Data

Currently, many of the traditional big data solutions that make real-time decisions require users to be very tech-savvy and require substantial coding. However, in 2016, we will probably see more companies purchasing tools that can be easily used by non-technical users. This is because there is currently a shortage of data scientists and the average salary of an entry level data scientist is quite high compared to that of an entry level analyst. Many companies just can’t afford to have data scientists on staff.  Also, customer facing groups want to be able to see results in real-time and not wait for the IT or data science group to get them the information they need. Solutions will still need to be set up by data scientists and software engineers, however, once the solution is set up, non-technical groups such as marketing and customer service will be the ones accessing the data and writing simple queries to find the information that they need in real-time.

2016 will definitely be an exciting time for big data! The Entrigna team is looking forward to working with companies in the next year to discover how we can help them make and achieve their big data goals! For more information on Entrigna please e-mail info@entrigna.com.