Grap analytics solutions have emerged as the best approach for discovery because they are designed from the outset to deal with the quantities of relationships, data, and queries needed. Graph solutions, which act like partners in the intuition of data analysts, enable organizations to rapidly sift through queries, redefine data sets, and chase possible fresh patterns of data and thinking—without worrying about redefining schema. While graph solutions have existed in niches like cybersecurity and intelligence for many years, the increasingly networked nature of the world, including social media, is shifting the graph approach from niche toward the mainstream. The good news is that as more organizations discover the power of big data discovery analytics, they will also discover fresh graph solutions in the market. While graph analytics solutions are unique compared to other BI/analytics solutions in that (a) they should optimally use large amounts of memory to perform their relationship-centric queries and (b) often require wide pipes for ingesting the potentially massive volumes of data they work with, they are no more difficult to use than other analytic solutions.